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Parameter Estimation for Discrete-Time Nonlinear Systems Using EM
University of Newcastle, Australia.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Newcastle, Australia.
2008 (English)In: Proceedings of the 17th IFAC World Congress, 2008, 4012-4017 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we consider parameter estimation of general stochastic nonlinear statespace models using the Maximum Likelihood method. This is accomplished via the employment of an Expectation Maximisation algorithm, where the essential components involve a particle smoother for the expectation step, and a gradient-based search for the maximisation step. The utility of this method is illustrated with several nonlinear and non-Gaussian examples.  

Place, publisher, year, edition, pages
2008. 4012-4017 p.
Keyword [en]
Nonlinear estimation, Maximum likelihood, System identification, Expectation maximisation
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-44269DOI: 10.3182/20080706-5-KR-1001.00675Local ID: 76146ISBN: 978-3-902661-00-5 (print)OAI: oai:DiVA.org:liu-44269DiVA: diva2:265131
Conference
17th IFAC World Congress, Seoul, South Korea, July, 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-02-23

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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
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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