Time series prediction using evolving radial basis function networks with new encoding scheme

Abstract

This paper presents a new encoding scheme for training radial basis function (RBF) networks by genetic algorithms (GAs). In general, it is very difficult to select the proper input variables and the exact number of nodes before training an RBF network. In the proposed encoding scheme, both the architecture (numbers and selections of nodes and inputs) and the parameters (centres and widths) of the RBF networks are represented in one chromosome and evolved simultaneously by GAs so that the selection of nodes and inputs can be achieved automatically. The performance and effectiveness of the presented approach are evaluated using two benchmark time series prediction examples and one practical application example, and are then compared with other existing methods. It is shown by the simulation tests that the developed evolving RBF networks are able to predict the time series accurately with the automatically selected nodes and inputs.