On the classification enhancement of radial basis function networks

Abstract

Artificial neural networks are powerfultools for analysing information expressed as data sets, which contain complex nonlinear relationships to be identified and classified. In particular radial basis function (RBF) neural networks have outstanding features for this. However, due to far reaching implications of the basis functions in the functionality of RBF networks they are still subject to study for best performance, in a general sense. One important parameter is the width of the radial basis functions. Here, we investigate the formation of a RBF neural network for its enhanced performance, which is closely related to the width parameter. For this aim, two key implementations are orthogonal least squares for training and multiresolutional decomposition of the sequence at the output of the network by wavelets.