C++ Neural Networks and Fuzzy Logic: Preface

Initializing Weights for Autoassociative Networks

Consider a network that is to associate each input pattern with itself and which gets binary patterns as inputs.

Make a bipolar mapping on the input pattern. That is, replace each 0 by –1. Call the mapped pattern the vector

x, when written as a column vector. The transpose, the same vector written as a row vector, is x

T

. You will get

a matrix of order the size of x when you form the product xx

T

. Obtain similar matrices for the other patterns

you want the network to store. Add these matrices to give you the matrix of weights to be used initially, as we

did in Chapter 4. This process can be described with the following equation: