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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Binary and Bipolar Inputs

Two types of inputs that are used in neural networks are binary and bipolar inputs. We have already seen

examples of binary input. Bipolar inputs have one of two values, 1 and –1. There is clearly a one−to−one

mapping or correspondence between them, namely having −1 of bipolar correspond to a 0 of binary. In

determining the weight matrix in some situations where binary strings are the inputs, this mapping is used,

and when the output appears in bipolar values, the inverse transformation is applied to get the corresponding

binary string. A simple example would be that the binary string 1 0 0 1 is mapped onto the bipolar string 1 –1

–1 1; while using the inverse transformation on the bipolar string –1 1 –1 –1, we get the binary string 0 1 0 0.

Bias

The use of threshold value can take two forms. One we showed in the example. The activation is compared to

the threshold value, and the neuron fires if the threshold value is attained or exceeded. The other way is to add

a value to the activation itself, in which case it is called the bias, and then determining the output of the

neuron. We will encounter bias and gain later.