Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning
Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning
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.
117-118
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
January 1995
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Chen, Sheng
(1995)
Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning.
Electronics Letters, 31 (2), .
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.
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Published date: January 1995
Organisations:
Southampton Wireless Group
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Local EPrints ID: 251082
URI: http://eprints.soton.ac.uk/id/eprint/251082
ISSN: 0013-5194
PURE UUID: 964f0b3b-bea4-4636-aa9d-9a4dc61f40a4
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Date deposited: 12 Oct 1999
Last modified: 14 Mar 2024 05:08
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Author:
Sheng Chen
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