On neural network design part II: Inhibition and the output map

Abstract

In this two part study, we presented a new design methodology for neural classifiers. The design procedure utilizes a multiclass vector quantization, MVQ, algorithm for information extraction from the training set. The extracted information suffices to specify the hidden layer in a canonical neural network architecture. The extracted information also leads to the specification of neuron inhibition rules and subsequently to the design of the hidden layer to output map. In part I of that study, we focused attention on the MVQ algorithm and how it is used to extract information from a training set. The extracted information is used to directly specify the hidden layer. In part II, we consider the non-simplistic hidden layer to output map design. We note that the MVQ algorithm, as it extracts information, decomposes the design set into disjoint neighborhoods. For each neighborhood we identify subsets of the hidden layer neurons which are significant sensors for the neighborhood. For each subset we construct an output map. Inhibition rules are established to assure that the proper output map is activated. In benchmark simulations, the overall design exhibits performance, to the extent that we are hard pressed to identify bounds on performance, if any.