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Blind Identification of Wiener Models
University of Newcastle, Australia.
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.
University of Newcastle, Australia.
2011 (English)In: Proceedings of the 18th IFAC World Congress, 2011, 5597-5602 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper develops and illustrates methods for the identification of Wiener model structures. These techniques are capable of accommodating the “blind” situation where the input excitation to the linear block is not observed. Furthermore, the algorithm developed here can accommodate a nonlinearity which need not be invertible, and may also be multivariable. Central to these developments is the employment of the Expectation Maximisation (EM) method for computing maximum likelihood estimates, and the use of a new approach to particle smoothing to efficiently compute stochastic expectations in the presence of nonlinearities.

Place, publisher, year, edition, pages
2011. 5597-5602 p.
Keyword [en]
Wiener model, Nonlinear systems, Maximum likelihood, System identification, Parameter estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-95593DOI: 10.3182/20110828-6-IT-1002.02610ISBN: 978-3-902661-93-7 (print)OAI: oai:DiVA.org:liu-95593DiVA: diva2:636431
Conference
18th IFAC World Congress, Milano, Italy, 28 August-2 September, 2011
Funder
Swedish Foundation for Strategic Research Swedish Research Council
Available from: 2013-07-10 Created: 2013-07-10 Last updated: 2013-07-10

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Schön, ThomasLjung, Lennart

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Citation style
  • apa
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  • modern-language-association-8th-edition
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
  • rtf