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Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

Wei, H.L. and Billings, S.A. (2009) Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach. International Journal of Control, 82 (1). pp. 27-42. ISSN 0020-7179


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In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance.

Item Type: Article
Copyright, Publisher and Additional Information: © 2009 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.
Keywords: cross-validation, model structure/subset selection, non-linear system identification, parameter estimation, random resampling, split-sample
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 11:54
Last Modified: 08 Feb 2013 16:58
Published Version: http://dx.doi.org/10.1080/00207170801955420
Status: Published
Publisher: Taylor & Francis
Refereed: Yes
Identification Number: 10.1080/00207170801955420
URI: http://eprints.whiterose.ac.uk/id/eprint/8552

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