This command trains a Backpropagation network using the Silva & Almeida training algorithm. The method is based on adaptive learning rate parameters on each neuron's weight. Unlike in the RPROP algorithm the maximum and minimum learning rate is not defined in this algorithm. By default one neuron for each input is used.
Example (ex5.10): Train a three-layered (input + hidden + output layer) network with a sine function using a sigmoid activation function in neurons. After training save the network output and plot the training error graph.
NDA> load sin.dat NDA> select sinx -f sin.x NDA> select siny -f sin.y NDA> sabp -di sinx -do siny -net 3 1 10 1 -types s s s -em 100 -nout wei -ef virhe -bs 1.0 -mup 1.1 -mdm 0.8 NDA> fbp -di sinx -do out -win wei NDA> select train -f virhe.TrainError NDA> select output -f sin.x out.0 NDA> save output NDA> mkgrp xxx NDA> ldgrv xxx -f virhe.TrainError -co black NDA> show xxx
