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

TLDR: The method uses a statistical linear regression technique which is based on the orthogonal least squares (OLS) algorithm, substituting a QR algorithm for the traditional Gram-Schmidt algorithm, to find the connected weight of the hidden layer neurons.

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.