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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)Report (Other academic)
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
Linköping: Linköping University Electronic Press, 2008. , 6 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2846
Keyword [en]
Nonlinear estimation, Maximum likelihood, System identification, Expectation maximisation
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-56160ISRN: LiTH-ISY-R-2846OAI: diva2:316940
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2016-04-22Bibliographically approved

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Schön, Thomas B.
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Automatic ControlThe Institute of Technology
Control Engineering

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ReferencesLink to record
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