Rule Extraction from Radial Basis Functional Neural Networks by Using Particle Swarm Optimization

Abstract

Radial basis functional neural networks (RBFNN) provide an outstanding possibility for generating rules for solving pattern classification problems. One of the most important factors in RBFNN is finding out the center and spread. This paper examines rules extracted from RBF networks trained by Particle swarm Optimization (PSO). The selection of the RBFNN centers, spreads and the network weights can be viewed as a system identification problem. Our Simulation results using Radial Basis Functional Neural Networks (RBFNN) was applied to the PAT, WBC and IRIS data sets as a classification problem to illustrate the new knowledge extraction technique. The results indicate that training RBFNN with PSO can provide comparable generalization of rules with less training time.