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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, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
University of Newcastle, Australia.
2009 (English)In: Proceedings of the 15th IFAC Symposium on System Identification, 2009, 810-815 p.Conference paper, Published paper (Refereed)
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
2009. 810-815 p.
Keyword [en]
Maximum likelihood methods, Bayesian methods, Nonlinear System Identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-45379DOI: 10.3182/20090706-3-FR-2004.00134Local ID: 82351OAI: oai:DiVA.org:liu-45379DiVA: diva2:266241
Conference
15th IFAC Symposium on System Identification, Saint-Malo, France, July, 2009
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-09-15

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Schön, Thomas

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  • apa
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  • asciidoc
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