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A Unified Approach to Identification of Linear SISO Models Subject to Missing Output Data and Missing Input Data
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.
2011 (English)Report (Other academic)
Abstract [en]

When output data is missing in a system identification scenario, it is not the Euclidean norm of the prediction error vector per se that should be minimized. Doing so will almost always yield biased parameter estimates. Two algorithms for estimation of the parameters, which can handle both missing output data and missing input data, are presented. The criterion minimized in the algorithms is the Euclidean norm of the prediction error vector scaled by a particular function of the covariance matrix of the observed output data. The algorithms yield a maximum likelihood estimate of the parameters under certain conditions.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 13 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3014
Keyword [en]
System identification, Maximum likelihood estimation, Missing data
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-97953ISRN: LiTH-ISY-R-3014OAI: oai:DiVA.org:liu-97953DiVA: diva2:650769
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-09-22Bibliographically approved

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Hansson, Anders

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
  • text
  • asciidoc
  • rtf