Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning

摘要

An improved clustering and recursive least squares (RLS) learning algorithm for Gaussian radial basis function (RBF) networks is described for modelling and predicting nonlinear time series. Significant performance gain can be achieved with a much smaller network compared with the usual clustering and RLS method.