/// <summary>
/// This method implements weight update procedure for the whole network for
/// this learning rule
/// </summary>
/// <param name="outputError">
/// output error vector </param>
protected internal override void updateNetworkWeights(double[] outputError)
{
int i = 0;
// for all output neurons
foreach (Neuron neuron in neuralNetwork.OutputNeurons)
{
// if error is zero, just set zero error and continue to next neuron
if (outputError[i] == 0)
{
neuron.Error = 0;
i++;
continue;
}
// otherwise calculate and set error/delta for the current neuron
TransferFunction transferFunction = neuron.TransferFunction;
double neuronInput = neuron.NetInput;
double delta = outputError[i] * transferFunction.getDerivative(neuronInput); // delta = (d-y)*df(net)
neuron.Error = delta;
// and update weights of the current neuron
this.updateNeuronWeights(neuron);
i++;
} // for
}
/// <summary>
/// Creates an instance of neuron for Hopfield network with specified input
/// and transfer functions </summary>
/// <param name="inFunc"> neuron input function </param>
/// <param name="transFunc"> neuron transfer function </param>
public InputOutputNeuron(InputFunction inFunc, TransferFunction transFunc) : base(inFunc, transFunc)
{
}
