A study of the radial basis function neural network classifiers using known data of varying accuracy and complexity

Abstract

Neural networks are increasingly used in a wide variety of applications such as speech recognition, diagnostic prediction, income prediction and credit screening. This paper empirically compares the performance of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks using artificially generated data sets, enabling us to accurately chart the effectiveness of each network type and to provide some guidance to practitioners as to which type of network to use with their data. We find that when the discriminator is simple, RBF and MLP network performances are similar; when the number of data points is relatively small the MLP outperforms the RBF; when the discriminator is complex the RBF outperforms the MLP; and when the data has an unrelated input and the underlying discriminator is simple, the MLP outperforms the RBF.