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Maximum Likelihood Estimation in Mixed Linear/Nonlinear State-Space Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2010 (English)Report (Other academic)
Abstract [en]

The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear state-space models, containing conditionally linear Gaussian substructures. More specifically, we employ the standard maximum likelihood framework and derive an expectation maximization type algorithm. This involves a nonlinear smoothing problem for the state variables, which for the conditionally linear Gaussian system can be efficiently solved using so called Rao-Blackwellized particle smoother (RBPS). As a secondary contribution of this paper we extend an existing RBPS to be able to handle the fully interconnected model under study.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2958
Keyword [en]
Nonlinear system identification- -Expectation maximization--Particle smoothing--Rao-Blackwellization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-97596ISRN: LiTH-ISY-R-2958OAI: oai:DiVA.org:liu-97596DiVA: diva2:649227
Funder
Swedish Foundation for Strategic Research Swedish Research Council
Available from: 2013-09-17 Created: 2013-09-17 Last updated: 2014-09-01Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
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  • Other style
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  • de-DE
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Output format
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