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On maximum likelihood identification of errors-in-variables models
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
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2017 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 2824-2829Article in journal (Refereed) Published
Abstract [en]

In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 50, no 1, p. 2824-2829
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Control Engineering
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URN: urn:nbn:se:kth:diva-223057DOI: 10.1016/j.ifacol.2017.08.634Scopus ID: 2-s2.0-85031797320OAI: oai:DiVA.org:kth-223057DiVA, id: diva2:1182470
Note

QC 20180213

Available from: 2018-02-13 Created: 2018-02-13 Last updated: 2018-02-13Bibliographically approved

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