DocumentCode
1243335
Title
Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning
Author
Chen, S.
Author_Institution
Dept. of Electr. & Electron. Eng., Portsmouth Univ., UK
Volume
31
Issue
2
fYear
1995
fDate
1/19/1995 12:00:00 AM
Firstpage
117
Lastpage
118
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;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:19950085
Filename
364358
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