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

C.L. Lin, J.F. Wang, C.Y. Chen, C.W. Chen, C.W. Yen
2009 Expert systems with applications  

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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
more » ... 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.
doi:10.1016/j.eswa.2009.03.012 fatcat:5v72qrljtzcsxfnzepugtwxygi
Citation

C.L. Lin, J.F. Wang, C.Y. Chen, C.W. Chen, C.W. Yen. "Improving the generalization performance of RBF neural networks using a linear regression technique." Expert systems with applications 36.10 (2009) 12049-12053