Improving the generalization performance of RBF neural networks using a linear regression technique

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• 作者：C.L. Lin ; J.F. Wang ; C.Y. Chen ; C.W. Chen ; C.W. Yen
• 关键词：Radial basis function ; Neural network ; Function approximation ; Generalization performance ; Orthogonal least squares
• 刊名：Expert Systems with Applications
• 出版年：2009
• 出版时间：December 2009
• 年：2009
• 卷：36
• 期：10
• 页码：12049-12053
• 全文大小：358 K

文摘

In this paper we present a method for improving the generalization performance of a radial basis function (RBF) neural network. The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm. We first discuss a modified way to determine the center and width of the hidden layer neurons. Then, substituting a QR algorithm for the traditional Gram–Schmidt algorithm, we find the connected weight of the hidden layer neurons. Cross-validation is utilized to determine the stop training criterion. The generalization performance of the network is further improved using a bootstrap technique. Finally, the solution method is used to solve a simulation and a real problem. The results demonstrate the improved generalization performance of our algorithm over the existing methods.