Extraction of similarity based fuzzy rules from artificial neural networks

Abstract

A method to extract a fuzzy rule based system from a trained artificial neural network for classification is presented. The fuzzy system obtained is equivalent to the corresponding neural network. In the antecedents of the fuzzy rules, it uses the similarity between the input datum and the weight vectors. This implies rules highly understandable. Thus, both the fuzzy system and a simple analysis of the weight vectors are enough to discern the hidden knowledge learnt by the neural network. Several classification problems are presented to illustrate this method of knowledge discovery by using artificial neural networks.