The University of Southampton
University of Southampton Institutional Repository

Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks

Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.
1239-1243
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wu, Y.
84854e37-ada6-4cc8-995f-6ce5ebc77423
Luk, B.L.
675a0272-9c35-466d-920f-8b86410b55ff
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Wu, Y.
84854e37-ada6-4cc8-995f-6ce5ebc77423
Luk, B.L.
675a0272-9c35-466d-920f-8b86410b55ff

Chen, S., Wu, Y. and Luk, B.L. (1999) Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Transactions on Neural Networks, 10 (5), 1239-1243.

Record type: Article

Abstract

The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.

Text
chen2.pdf - Other
Download (98kB)

More information

Published date: September 1999
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251051
URI: http://eprints.soton.ac.uk/id/eprint/251051
PURE UUID: c74f6e13-deaa-435b-9986-9700eb631aca

Catalogue record

Date deposited: 13 Oct 1999
Last modified: 16 Dec 2019 20:47

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×