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On model order priors for Bayesian identification of SISO linear systems
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
Uppsala Univ, Dept Informat Technol, Uppsala, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-0355-2663
2019 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 92, no 7, p. 1645-1661Article in journal (Refereed) Published
Abstract [en]

A method for the identification of single input single output linear systems is presented. The method employs a Bayesian approach to compute the posterior distribution of the model parameters given the data-set. Since this distribution is often unavailable in closed form, a Metropolis Hastings algorithm is implemented to draw samples from it. To implement the sampler, the inclusion of prior information regarding the model order of the identified system is discussed. As one of the main contributions of this work, a prior over the Hankel singular values of the model is imposed. Numerical examples illustrate the method.

Place, publisher, year, edition, pages
Taylor & Francis, 2019. Vol. 92, no 7, p. 1645-1661
Keywords [en]
System identification, Bayesian estimation, Metropolis Hastings sampler, model order prior
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-255303DOI: 10.1080/00207179.2017.1406147ISI: 000472558100017Scopus ID: 2-s2.0-85067805258OAI: oai:DiVA.org:kth-255303DiVA, id: diva2:1339611
Note

QC 20190730

Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-07-30Bibliographically approved

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Rojas, Cristian R.

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CiteExportLink to record
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