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Sliced Inverse Regression for the Identification of Dynamical Systems
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]

The estimation of nonlinear functions can be challenging when the number of independent variables is high. This difficulty may, in certain cases, be reduced by first projecting the independent variables on a lower dimensional subspace before estimating the nonlinearity. In this paper, a statistical nonparametric dimension reduction method called sliced inverse regression is presented and a consistency analysis for dynamically dependent variables is given. The straightforward system identification application is the estimation of the number of linear subsystems in a Wiener class system and their corresponding impulse response.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 16 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3031
Keyword [en]
Nonlinear System Identification, Nonparametric Methods
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-97973ISRN: LiTH-ISY-R-3031OAI: oai:DiVA.org:liu-97973DiVA: diva2:650871
Available from: 2013-09-23 Created: 2013-09-23 Last updated: 2014-09-01Bibliographically approved

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Lyzell, ChristianEnqvist, Martin

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CiteExportLink to record
Permanent link

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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