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A new kernel-based approach to overparameterized Hammerstein system identification
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)ORCID iD: 0000-0002-2831-2909
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. (System Identification)
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control. (System Identification)ORCID iD: 0000-0002-9368-3079
2015 (English)In: 2015 54th IEEE Conference on Decision and Control (CDC), IEEE conference proceedings, 2015, 115-120 p.Conference paper, Published paper (Refereed)
Abstract [en]
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The object of this paper is the identification of Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of p basis functions. We model the system dynamics by means of an np-dimensional vector. This vector, usually referred to as overparameterized vector, contains all the combinations between the nonlinearity coefficients and the first n samples of the impulse response of the linear block. The estimation of the overparameterized vector is performed with a new regularized kernel-based approach. To this end, we introduce a novel kernel tailored for overparameterized models, which yields estimates that can be uniquely decomposed as the combination of an impulse response and p coefficients of the static nonlinearity. As part of the work, we establish a clear connection between the proposed identification scheme and our recently developed nonparametric method based on the stable spline kernel.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 115-120 p.
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-186520DOI: 10.1109/CDC.2015.7402095ISI: 000381554500036ISBN: 978-1-4799-7884-7 (print)OAI: oai:DiVA.org:kth-186520DiVA: diva2:927534
Conference
2015 54th IEEE Conference on Decision and Control (CDC),15-18 Dec. 2015, Osaka, Japan
Note

QC 20160520

Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2016-12-22Bibliographically approved

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Risuleo, Riccardo SvenHjalmarsson, Håkan

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