C++ Neural Networks and Fuzzy Logic: Preface

C++ Neural Networks and Fuzzy Logic

by Valluru B. Rao

MTBooks, IDG Books Worldwide, Inc.

ISBN: 1558515526   Pub Date: 06/01/95

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Adjustments to Threshold Values or Biases

The bias or the threshold value we added to the activation, before applying the threshold function to get the

output of a neuron, will also be adjusted based on the error being propagated back. The needed values for this

are in the previous discussion.

The adjustment for the threshold value of a neuron in the output layer is obtained by multiplying the

calculated error (not just the difference) in the output at the output neuron and the learning rate parameter used

in the adjustment calculation for weights at this layer. In our previous example, we have the learning rate

parameter as 0.2, and the error vector as (–0.02, –0.04, 0.04, 0.11), so the adjustments to the threshold values

of the four output neurons are given by the vector (–0.004, –0.008, 0.008, 0.022). These adjustments are

added to the current levels of threshold values at the output neurons.

The adjustment to the threshold value of a neuron in the hidden layer is obtained similarly by multiplying the

learning rate with the computed error in the output of the hidden layer neuron. Therefore, for the second

neuron in the hidden layer, the adjustment to its threshold value is calculated as 0.15 * –0.0041, which is

–0.0006. Add this to the current threshold value of 0.679 to get 0.6784, which is to be used for this neuron in

the next training pattern for the neural network.