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NARX-based nonlinear system identification using orthogonal least squares basis hunting

NARX-based nonlinear system identification using orthogonal least squares basis hunting
NARX-based nonlinear system identification using orthogonal least squares basis hunting
An orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems. Unlike most of the existing RBF or kernel modelling methods, which
places the RBF or kernel centers at the training input data points and use a fixed common variance for all the regressors, the proposed OLS-BH technique tunes the RBF center and diagonal covariance matrix of individual regressor by minimizing the training mean square error. An efficient optimization method is
adopted for this basis hunting to select regressors in an orthogonal forward selection procedure. Experimental results obtained using this OLS-BH technique demonstrate that it offers a state-of-the-art method for constructing parsimonious RBF models with excellent generalization performance.
1063-6536
78-84
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wang, X.X.
38cba1ba-039f-427c-ac6f-459768df3834
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d

Chen, Sheng, Wang, X.X. and Harris, Chris J. (2008) NARX-based nonlinear system identification using orthogonal least squares basis hunting. IEEE Transactions on Control Systems Technology, 16 (1), 78-84. (doi:10.1109/TCST.2007.899728).

Record type: Article

Abstract

An orthogonal least squares technique for basis hunting (OLS-BH) is proposed to construct sparse radial basis function (RBF) models for NARX-type nonlinear systems. Unlike most of the existing RBF or kernel modelling methods, which
places the RBF or kernel centers at the training input data points and use a fixed common variance for all the regressors, the proposed OLS-BH technique tunes the RBF center and diagonal covariance matrix of individual regressor by minimizing the training mean square error. An efficient optimization method is
adopted for this basis hunting to select regressors in an orthogonal forward selection procedure. Experimental results obtained using this OLS-BH technique demonstrate that it offers a state-of-the-art method for constructing parsimonious RBF models with excellent generalization performance.

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Published date: 3 January 2008
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 264991
URI: http://eprints.soton.ac.uk/id/eprint/264991
ISSN: 1063-6536
PURE UUID: dd5f6802-fcc1-4512-b851-c4399d331540

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Date deposited: 02 Jan 2008 09:14
Last modified: 14 Apr 2021 16:42

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