Fuzzy Rule Acquisition from Trained Artificial Neural Networks

Abstract

We approach the problem of rule extraction in its primary form. That is, given a trained artificial neural network, we extract rules classifying data set as correctly as possible. Attention is oriented toward extraction of fuzzy rules. The choice of fuzzy rules underlines the aim of balancing rule comprehensibility and complexity. To achieve higher comprehensibility of extracted rules, the formulated theoretical material is an extension of crisp rule extraction 1). A rule extraction algorithm is introduced. The presented algorithm for fuzzy rule extraction implies from the derived theoretical results rather than from heuristics. The rule extraction algorithm incorporates a ’built-in’ rule simplification mechanism. This feature is beneficial in cases when trained neural network structure is overdetermined for a given task. The rule extraction algorithm is experimentally demonstrated. Demonstrations incorporate both structure modification training and fixed structure training.