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Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
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2012 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 20, no 11, 1165-1174 p.Article in journal (Refereed) Published
Abstract [en]

This paper considers the identification of Wiener-Hammerstein systems using Least-Squares Support Vector Machines based models. The power of fully black-box NARX-type models is evaluated and compared with models incorporating information about the structure of the systems. For the NARX models it is shown how to extend the kernel-based estimator to large data sets. For the structured model the emphasis is on preserving the convexity of the estimation problem through a suitable relaxation of the original problem. To develop an empirical understanding of the implications of the different model design choices, all considered models are compared on an artificial system under a number of different experimental conditions. The obtained results are then validated on the Wiener-Hammerstein benchmark data set and the final models are presented. It is illustrated that black-box models are a suitable technique for the identification of Wiener-Hammerstein systems. The incorporation of structural information results in significant improvements in modeling performance.

Place, publisher, year, edition, pages
2012. Vol. 20, no 11, 1165-1174 p.
Keyword [en]
Nonlinear system identification, LS-SVMs, Kernel-based models, Overparameterization, Large-scale data processing
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-185178DOI: 10.1016/j.conengprac.2012.05.006ISI: 000309847800010OAI: oai:DiVA.org:uu-185178DiVA: diva2:571501
Available from: 2012-11-22 Created: 2012-11-21 Last updated: 2017-12-07Bibliographically approved

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Pelckmans, Kristiaan

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