An efficient learning algorithm for improving generalization performance of radial basis function neural networks

doi:10.1016/S0893-6080(00)00029-0

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An efficient learning algorithm for improving generalization performance of radial basis function neural networks

2000-06-01

PRE

Wang Zheng-ou *

竹涛 (Τao Zhu)

DOI：10.1016/S0893-6080(00)00029-0

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Abstract

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This paper presents an efficient recursive learning algorithm for improving generalization performance of radial basis function (RBF) neural networks. The approach combines the rival penalized competitive learning (PRCL) [Xu, L., Kizyzak, A. & Oja, E. (1993). Rival penalized competitive learning for clustering analysis, RBF net and curve detection, IEEE Transactions on Neural Networks, 4, 636-649] and the regularized least squares (RLS) to provide an efficient and powerful procedure for constructing a minimal RBF network that generalizes very well. The RPCL selects the number of hidden units of network and adjusts centers, while the RLS constructs the parsimonious network and estimates the connection weights. In the RLS we derived a simple recursive algorithm, which needs no matrix calculation, and so largely reduces the computational cost. This combined algorithm significantly enhances the generalization performance and the real-time capability of the RBF networks. Simulation results of three different problems demonstrate much better generalization performance of the present algorithm over other existing similar algorithms. (C) 2000 Elsevier Science Ltd. All rights reserved.

Keywords:

radial basis function neural network

generalization performance

rival penalized competitive learning

regularized least squares

recursive algorithm

real-time capability

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