New RBF neural network classifier with optimized hidden neurons number

This article presents a noticeable performances improvement of a neural classifier based on an RBF network. Based on the Mahalanobis distance, this new classifier increases relatively the recognition rate while decreasing remarkably the number of hidden layer neurons. We obtain thus a new very general RBF classifier, very simple, not requiring any adjustment parameter, and presenting an excellent ratio performances/neurons number. A comparative study of its performances is presented and illustrated by examples on artificial and real databases. Key-Words: RBF neural networks, Mahalanobis distance, clustering, training algorithms, hidden neurons number optimization, burying tag identification.