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Maximum Likelihood Identification of Wiener Models with a Linear Regression Initialization
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.
1998 (English)Report (Other academic)
Abstract [en]

Many parametric identification routines suffer from the problem with local minima. This is true also for the prediction-error approach to identifying Wiener models, i.e. linear models with a static non-linearity at the output. We here suggest a linear regression initialization, that secures a consistent and efficient estimate, when used in conjunction with a Gauss-Newton minimization scheme.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 1998. , 2 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2051
Keyword [en]
Nonlinear identification, Wiener models, Linear systems, Maximum likelihood estimation, Recursive estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55656ISRN: LiTH-ISY-R2051OAI: oai:DiVA.org:liu-55656DiVA: diva2:316417
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-12Bibliographically approved

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Maximum Likelihood Identification of Wiener Models with a Linear Regression Initialization(145 kB)57 downloads
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Hagenblad, AnnaLjung, Lennart

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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
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  • asciidoc
  • rtf