Title :
Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning
Author_Institution :
Dept. of Electr. & Electron. Eng., Portsmouth Univ., UK
fDate :
1/19/1995 12:00:00 AM
Abstract :
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
Keywords :
learning (artificial intelligence); least squares approximations; modelling; neural nets; prediction theory; time series; Gaussian RBF networks; RLS learning; enhanced clustering; modelling; nonlinear time series; prediction; radial basis function; recursive least squares algorithm;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19950085
