The effect of several parameters on radial basis function networks for time series prediction

Abstract

In this study, several radial basis function networks are compared according to their approximation ability in time series forecasting problems. Optimal values for the tested parameters are obtained using computer simulation runs. Effects of width selection in Gaussian Kemels, of the number of neurons in the hidden layer, and of selection of Kemel function are investigated.