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

Authors:
C. L. Lin;J. F. Wang;C. Y. Chen;C. W. Chen;C. W. Yen
Affiliations:
Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80441, Taiwan;Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80441, Taiwan;Department of Management Information System, Yung-Ta Institute of Technology and Commerce, 316 Jong Shan, Rd., Lin Luoh, Pingtung 90941, Taiwan and Department of Computer Science, National Pingtun ...;Department of Logistics Management, Shu-Te University, Kaohsiung 82445, Taiwan;Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung 80441, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.