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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Chapter 4

Constructing a Neural Network

First Example for C++ Implementation

The neural network we presented in Chapter 1 is an example of a Hopfield network with a single layer. Now

we present a C++ implementation of this network. Suppose we place four neurons, all connected to one

another on this layer, as shown in Figure 4.1. Some of these connections have a positive weight and the rest

have a negative weight. You may recall from the earlier presentation of this example, that we used two input

patterns to determine the weight matrix. The network recalls them when the inputs are presented to the

network, one at a time. These inputs are binary and orthogonal so that their stable recall is assured. Each

component of a binary input pattern is either a 0 or a 1. Two vectors are orthogonal when their dot

product—the sum of the products of their corresponding components—is zero. An example of a binary input

pattern is 1 0 1 0 0. An example of a pair of orthogonal vectors is (0, 1, 0, 0, 1) and (1, 0, 0, 1, 0). An example

of a pair of vectors that are not orthogonal is (0, 1, 0, 0, 1) and (1, 1, 0, 1, 0). These last two vectors have a dot

product of 1, different from 0.