Hybrid Learning of RBF Networks

Abstract

Three different learning methods for RBF networks and their combinations are presented. Standard gradient learning, three-step algoritm with unsupervised part, and evolutionary algorithm are introduced. Their perfromance is compared on two benchmark problems: Two spirals and Iris plants. The results show that three-step learning is usually the fastest, while gradient learning achieves better precission. The combination of these two approaches gives best results.