FIRNet Usage

firnet -f filename [switches...]

Switches:
    -l layers      # of layers in the FIR network: 2 (default) or 3
    -n n1 [n2]     # of nodes in each hidden layer
    -t n1 n2 [n3]  # of taps in each layer
    -p start end   Range of time series values to predict
    -e epochs      # of epochs to train for
    -c             Force training (if disabled by loading state)
    -r rate        Learning rate
    -m alpha       Use momentum with parameter alpha in [0,1]
    -d             Linearly decrease learning rate

Notes:

• Time series files are formatted as n-dimensional real vectors separated by linefeeds.
• For an input file named series.dat, there are two corresponding files: series.net, the neural network state, and series.out, the predicted output time series.
• If a .net file does not exist for an input file, FIRNet will train a network on the input series, save the network state, and then continue on to the prediction phase.
• If a .net file does exist (and the -c switch is not invoked), training will be skipped and the network state in the .net file will be used for prediction.
• FIRNet only implements the two special case networks having 2 and 3 layers. (Note: the usage of "layer" here differs from standard neural network parlance -- it refers to the set of synapses in between nodes. So, a 2 layer FIR net really has 3 distinct sets of nodes.)
• The time tap parameters (set with the -t switch) refer to the size of the FIR filters on each layer's synapses.

The FIRNet Page