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Order and Structural Dependence Selection of LPV-ARX Models using a Nonnegative Garrote Approach
Delft University of Technology, The Netherlands.
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.
Delft University of Technology, The Netherlands.
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2010 (English)Report (Other academic)
Abstract [en]

In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 9 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2937
Keyword [en]
ARX- -Identification--Linear parameter-varying--Order selection
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-97548ISRN: LiTH-ISY-R-2937OAI: oai:DiVA.org:liu-97548DiVA: diva2:648392
Available from: 2013-09-16 Created: 2013-09-16 Last updated: 2014-09-01Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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