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Using Manifold Learning for Nonlinear System Identification
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.
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.
2007 (English)Report (Other academic)
Abstract [en]

A high-dimensional regression space usually causes problems in nonlinear system identification.However, if the regression data are contained in (or spread tightly around) some manifold, thedimensionality can be reduced. This paper presents a use of dimension reduction techniques tocompose a two-step identification scheme suitable for high-dimensional identification problems withmanifold-valued regression data. Illustrating examples are also given.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2007. , 9 p.706-711 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2795
Keyword [en]
Nonlinear system identification; Dimension reduction techniques; Manifold learning
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56048ISRN: LiTH-ISY-R-2795OAI: oai:DiVA.org:liu-56048DiVA: diva2:316892
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-02Bibliographically approved

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Authority records BETA

Ohlsson, HenrikRoll, JacobGlad, TorkelLjung, Lennart

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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