The University of Southampton
University of Southampton Institutional Repository

Non-linear system identification using particle swarm optimisation tuned radial basis function models

Non-linear system identification using particle swarm optimisation tuned radial basis function models
Non-linear system identification using particle swarm optimisation tuned radial basis function models
A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of nonlinear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable-node RBF models is demonstrated using three real data sets
1758-0366
246-258
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Luk, Bing L.
7f992721-74f4-4a2d-b990-afcece627189
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Luk, Bing L.
7f992721-74f4-4a2d-b990-afcece627189
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia, Luk, Bing L. and Harris, Chris J. (2009) Non-linear system identification using particle swarm optimisation tuned radial basis function models. International Journal of Bio-Inspired Computation, 1 (4), 246-258. (doi:10.1504/IJBIC.2009.024723).

Record type: Article

Abstract

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of nonlinear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable-node RBF models is demonstrated using three real data sets

PDF IJBIC010403CHEN.pdf - Version of Record
Download (377kB)

More information

Published date: 2009
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 267296
URI: https://eprints.soton.ac.uk/id/eprint/267296
ISSN: 1758-0366
PURE UUID: c1ee18cf-d4d3-44fd-98cb-c488acd2ea5b

Catalogue record

Date deposited: 27 Apr 2009 09:50
Last modified: 18 Jul 2017 07:06

Export record

Altmetrics

Contributors

Author: Sheng Chen
Author: Xia Hong
Author: Bing L. Luk
Author: Chris J. Harris

University divisions

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

Library staff edit
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 https://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.

×