Research Papers

Nonlinear Model Identification From Multiple Data Sets Using an Orthogonal Forward Search Algorithm

[+] Author and Article Information
Ping Li

e-mail: p.li@sheffield.ac.uk

Hua-Liang Wei

e-mail: w.hualiang@sheffield.ac.uk

Stephen A. Billings

e-mail: s.billings@sheffield.ac.uk

Michael A. Balikhin

e-mail: m.balikhin@sheffield.ac.uk

Richard Boynton

e-mail: r.boynton@sheffield.ac.uk
Department of Automatic Control and Systems Engineering,
The University of Sheffield Sheffield S10 2TN,

Contributed by the Design Engineering Division of ASME for publication in the Journal of Computational and Nonlinear Dynamics. Manuscript received October 4, 2011; final manuscript received February 22, 2013; published online March 26, 2013. Assoc. Editor: Jozsef Kovecses.

J. Comput. Nonlinear Dynam 8(4), 041001 (Mar 26, 2013) (10 pages) Paper No: CND-11-1168; doi: 10.1115/1.4023864 History: Received October 04, 2011; Revised January 21, 2013

A basic assumption on the data used for nonlinear dynamic model identification is that the data points are continuously collected in chronological order. However, there are situations in practice where this assumption does not hold and we end up with an identification problem from multiple data sets. The problem is addressed in this paper and a new cross-validation-based orthogonal search algorithm for NARMAX model identification from multiple data sets is proposed. The algorithm aims at identifying a single model from multiple data sets so as to extend the applicability of the standard method in the cases, such as the data sets for identification are obtained from multiple tests or a series of experiments, or the data set is discontinuous because of missing data points. The proposed method can also be viewed as a way to improve the performance of the standard orthogonal search method for model identification by making full use of all the available data segments in hand. Simulated and real data are used in this paper to illustrate the operation and to demonstrate the effectiveness of the proposed method.

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Grahic Jump Location
Fig. 1

The two input-output data sets for modeling

Grahic Jump Location
Fig. 2

Comparison between the measured Dst and the model predicted D∧st obtained with D1 only for two events

Grahic Jump Location
Fig. 3

Comparison between the measured Dst and the model predicted D∧st obtained with D2 only for two events

Grahic Jump Location
Fig. 4

Comparison between the measured Dst and the model predicted D∧st obtained with D1 and D2 for two events



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