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Particle Filter Approach to Nonlinear System Identification under Missing Observations with a Real Application
University of British Columbia, Canada.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Newcastle, Australia.
2009 (English)Report (Other academic)
Abstract [en]

This article reviews authors' recently developed algorithm for identification of nonlinear state-space models under missing observations and extends it to the case of unknown model structure. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. If the model structure is unknown, an approximation of it is obtained using radial basis functions centered around a maximum a posteriori estimate of the state trajectory. A particle filter approximation of smoothed states is then used in conjunction with expectation maximization algorithm for estimating the parameters. The proposed approach is illustrated through a real application.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2895
Keyword [en]
Maximum likelihood methods, Bayesian methods, Nonlinear System Identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56202ISRN: LiTH-ISY-R-2895OAI: oai:DiVA.org:liu-56202DiVA: diva2:317008
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-11Bibliographically approved

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Schön, Thomas

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf