Successive approximation radial basis function networks for nonlinear modeling and prediction

Abstract

In the paper, a multilayer radial basis function network is proposed for nonlinear time series modeling and prediction. It uses successive approximations, first obtaining a number of coarse approximations, and then optimally linearly combining the coarsely defined functions to achieve an accurate end result. Compared with the conventional approaches using radial basis functions, the present method considerably reduces computation time, and can improve the predictive ability of radial basis function networks while retaining good training accuracy. The method is particularly useful for modeling and prediction of nearly cyclical nonlinear time series in the presence of observational noise. Numerical examples for chaotic time series and some classical real world time series are presented. It is shown that the successive approximation radial basis function network presented in the paper is a very useful tool for nonlinear modeling and prediction.