Title: Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information

Authors: Hua-Liang Wei, Stephen A. Billings

Addresses: Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK. ' Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK

Abstract: Model structure selection plays a key role in non-linear system identification. The first step in non-linear system identification is to determine which model terms should be included in the model. Once significant model terms have been determined, a model selection criterion can then be applied to select a suitable model subset. The well known Orthogonal Least Squares (OLS) type algorithms are one of the most efficient and commonly used techniques for model structure selection. However, it has been observed that the OLS type algorithms may occasionally select incorrect model terms or yield a redundant model subset in the presence of particular noise structures or input signals. A very efficient Integrated Forward Orthogonal Search (IFOS) algorithm, which is assisted by the squared correlation and mutual information, and which incorporates a Generalised Cross-Validation (GCV) criterion and hypothesis tests, is introduced to overcome these limitations in model structure selection.

Keywords: orthogonal search; squared correlation; hypothesis tests; nonlinear systems; system identification; model selection; mutual information; NARX-NARMAX model; model structure.

DOI: 10.1504/IJMIC.2008.020543

International Journal of Modelling, Identification and Control, 2008 Vol.3 No.4, pp.341 - 356

Published online: 29 Sep 2008 *

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