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Using Manifold Learning for Nonlinear System Identification
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. Linköping University, Department of Electrical Engineering, Automatic Control.
2007 (English)In: Proceedings of the 7th IFAC Symposium on Nonlinear Control Systems, 2007, 170-175 p.Conference paper, Published paper (Refereed)
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
2007. 170-175 p.
Keyword [en]
Nonlinear system identification, Dimension reduction techniques, Manifold learning
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-42219DOI: 10.3182/20070822-3-ZA-2920.00029Local ID: 61637ISBN: 978-3-902661-28-9 (print)OAI: oai:DiVA.org:liu-42219DiVA: diva2:263074
Conference
7th IFAC Symposium on Nonlinear Control Systems, Pretoria, South Africa, August, 2007
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-02-26

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Ohlsson, HenrikRoll, JacobGlad, TorkelLjung, Lennart

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Citation style
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Language
  • de-DE
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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