Minimal RBF networks by Gaussian mixture model

Abstract

Radial basis function (RBF) networks have been successfully applied to function interpolation and classification problems among others. In this paper, we propose a basis function optimization method using a mixture density model. We generalize the Gaussian radial basis functions to arbitrary covariance matrices, in order to fully utilize the Gaussian probability density function. We also try to achieve a parsimonious network topology by using a systematic procedure. According to experimental results, the proposed method achieved fairly comparable performance with smaller number of hidden layer nodes to the conventional approach in terms of correct classification rates. © Springer-Verlag Berlin Heidelberg 2005.