Table of Contents
ISRN Applied Mathematics
Volume 2012 (2012), Article ID 324194, 34 pages
Review Article

Using Radial Basis Function Networks for Function Approximation and Classification

1Enjoyor Laboratories, Enjoyor Inc., Hangzhou 310030, China
2Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada H3G 1M8

Received 16 November 2011; Accepted 5 December 2011

Academic Editors: E. Gallopoulos, E. Kita, and M. Sun

Copyright © 2012 Yue Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.