Affiliation:
1. Enjoyor Laboratories, Enjoyor Inc., Hangzhou 310030, China
2. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada H3G 1M8
Abstract
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.
Funder
Natural Sciences and Engineering Research Council of Canada
Reference154 articles.
1. Institute of Mathematics & Its Applications Conference Series,1987
2. Networks for approximation and learning
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