A self-organizing HCMAC neural network classifier

Abstract

This study presents a self-organizing hierarchical CMAC neural network classifier which contains a self-organizing input space module and a hierarchical CMAC neural network. However, the conventional CMAC has an enormous memory requirement, and its performance heavily depends on the approach of input space quantization. To solve these problems, this study presents a novel hierarchical CMAC neural network module capable of resolving both the enormous memory requirement in the conventional CMAC and high dimensional problems. Also proposed herein is a self-organizing input space module that uses Shannon's entropy measure and the golden section search method to appropriately determine the input space quantization according to the distribution of training data sets. Experimental results indicate that the self-organizing HCMAC indeed has a fast learning ability and low memory requirement. Moreover, the self-organizing HCMAC classifier has a better classification ability than other classifiers.