C++ Neural Networks and Fuzzy Logic: Preface

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Weight Initialization for Heteroassociative Networks

Consider a network that is to associate one input pattern with another pattern and that 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. Get a similar bipolar mapping for the corresponding associated

pattern. Call it y. You will get a matrix of size x by size y when you form the product xy

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. The following equation restates this process: