Title :
Evolving Gaussian RBF network for nonlinear time series modelling and prediction
Author :
Aiguo, Song ; Jiren, Lu
Author_Institution :
Dept. of Radio Eng., Southeast Univ., Nanjing, China
fDate :
6/11/1998 12:00:00 AM
Abstract :
A genetic algorithm and recursive least squares (RLS) learning algorithm for a Gaussian radial basis function network is described, for modelling and predicting nonlinear time series. Better generalisation performance can be achieved than that of the usual clustering and RLS method
Keywords :
feedforward neural nets; genetic algorithms; learning (artificial intelligence); least squares approximations; prediction theory; time series; Gaussian radial basis function network; evolution; genetic algorithm; modelling; nonlinear time series; prediction; recursive least squares learning algorithm;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19980839
