Determining the structure of a radial basis function network for prediction of nonlinear hydrological time series

Abstract

A network using radial basis functions (RBFs) as the mapping function in the evolutionary equation for prediction of time series is presented. An R3F networ requires the determination of the number of centres of RBFs, their receptive field widths and the linear weights of the network output layer. Traditionally, the number of centres of RBFs is fixed. In this paper, methods to estimate the widths of the receptives fields and the number of centres for the RBFs are introduced. The latter is based on the concept of the generalized degrees of freedom. The linear weights am determined by the leastsquares method. The proposed method is then applied to make predictions of six sets of data: two theoretical functions that are known to become chaotic under certain parameter conditions (Henon map and Lorenz map), and four real-time series (discharge data from the Mekong River in Thailand and Laos, and from the Chao Phraya River in Thailand, and sea-surface temperature anomaly data). The results are at least one order of magnitude better than those obtained by a similar model with fixed number of centres as well as by a linear model and a stochastic model for most of the data sets. Copyright © 2006 IAHS Press.