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An adaptive orthogonal search algorithm for model subset selection and non-linear system identification

Wei, H.L. and Billings, S.A. (2008) An adaptive orthogonal search algorithm for model subset selection and non-linear system identification. International Journal of Control, 81 (5). pp. 714-724. ISSN 0020-7179

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A new adaptive orthogonal search (AOS) algorithm is proposed for model subset selection and non-linear system identification. Model structure detection is a key step in any system identification problem. This consists of selecting significant model terms from a redundant dictionary of candidate model terms, and determining the model complexity (model length or model size). The final objective is to produce a parsimonious model that can well capture the inherent dynamics of the underlying system. In the new AOS algorithm, a modified generalized cross-validation criterion, called the adjustable prediction error sum of squares (APRESS), is introduced and incorporated into a forward orthogonal search procedure. The main advantage of the new AOS algorithm is that the mechanism is simple and the implementation is direct and easy, and more importantly it can produce efficient model subsets for most non-linear identification problems.

Item Type: Article
Copyright, Publisher and Additional Information: © 2008 Taylor & Francis. This is an author produced version of a paper subsequently published in International Journal of Control. Uploaded in accordance with the publisher's self-archiving policy.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 30 Apr 2009 11:19
Last Modified: 08 Feb 2013 16:58
Published Version: http://dx.doi.org/10.1080/00207170701216311
Status: Published
Publisher: Taylor & Francis
Refereed: Yes
Identification Number: 10.1080/00207170701216311
URI: http://eprints.whiterose.ac.uk/id/eprint/8555

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