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Nonlinear FIR Identification with Model Order Reduction Steiglitz-McBride⁎
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0002-9368-3079
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 646-651Article in journal (Refereed) Published
Abstract [en]

In system identification, many structures and approaches have been proposed to deal with systems with non-linear behavior. When applicable, the prediction error method, analogously to the linear case, requires minimizing a cost function that is non-convex in general. The issue with non-convexity is more problematic for non-linear models, not only due to the increased complexity of the model, but also because methods to provide consistent initialization points may not be available for many model structures. In this paper, we consider a non-linear rational finite impulse response model. We observe how the prediction error method requires minimizing a non-convex cost function, and propose a three-step least-squares algorithm as an alternative procedure. This procedure is an extension of the Model Order Reduction Steiglitz-McBride method, which is asymptotically efficient in open loop for linear models. We perform a simulation study to illustrate the applicability and performance of the method, which suggests that it is asymptotically efficient. 

Place, publisher, year, edition, pages
Elsevier B.V. , 2018. Vol. 51, no 15, p. 646-651
Keywords [en]
least-squares identification, non-linear, System identification, Cost functions, Identification (control systems), Impulse response, Model structures, Religious buildings, Asymptotically efficient, Finite impulse response model, Least squares algorithm, Least squares identification, Non linear, Nonconvex cost functions, Prediction error method, Steiglitz-Mcbride method, Least squares approximations
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247493DOI: 10.1016/j.ifacol.2018.09.218ISI: 000446599200110Scopus ID: 2-s2.0-85054449269OAI: oai:DiVA.org:kth-247493DiVA, id: diva2:1301980
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-05-20Bibliographically approved

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Ferizbegovic, MinaGalrinho, MiguelHjalmarsson, Håkan

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