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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Additional Issues

If you desire to use vectors with real number components, instead of binary numbers, you can do so. Your

model is then called a Continuous Bidirectional Associative Memory. A matrix W and its transpose are used

for the connection weights. However, the matrix W is not formulated as we described so far. The matrix is

arbitrarily chosen, and kept constant. The thresholding function is also chosen as a continuous function,

unlike the one used before. The changes in activations of neurons during training are according to extensions

the Cohen−Grossberg paradigm.

Michael A. Cohen and Stephen Grossberg developed their model for autoassociation, with a symmetric matrix

of weights. Stability is ensured by the Cohen−Grossberg theorem; there is no learning.

If, however, you desire the network to learn new pattern pairs, by modifying the weights so as to find

association between new pairs, you are designing what is called an Adaptive Bidirectional Associative

Memory (ABAM).

The law that governs the weight changes determines the type of ABAM you have, namely, the Competitive

ABAM, or the Differential Hebbian ABAM, or the Differential Competitive ABAM. Unlike in the ABAM

model, which is additive type, some products of outputs from the two layers, or the derivatives of the

threshold functions are used in the other models.

Here we present a brief description of a model, which is a variation of the BAM. It is called UBBAM

(Unipolar Binary Bidirectional Associative Memory).