Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems
2012 (English)In: Control Engineering Practice, ISSN 0967-0661, Vol. 20, no 11, 1165-1174 p.Article in journal (Refereed) Published
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.
Nonlinear system identification, LS-SVMs, Kernel-based models, Overparameterization, Large-scale data processing
IdentifiersURN: urn:nbn:se:uu:diva-185178DOI: 10.1016/j.conengprac.2012.05.006ISI: 000309847800010OAI: oai:DiVA.org:uu-185178DiVA: diva2:571501