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Input selection in ARX model estimation using group lasso regularization
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2018 (English)In: 18th IFAC Symposium on System Identification (SYSID), Proceedings, ELSEVIER SCIENCE BV , 2018, Vol. 51, no 15, p. 897-902Conference paper, Published paper (Refereed)
Abstract [en]

In system identification, input selection is a challenging problem. Since less complex models are desireable, non-relevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we investigate an input selection extension in least-squares ARX estimation and show that better model estimates are achieved compared to the least-square ssolution, in particular, for short batches of estimation data. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2018. Vol. 51, no 15, p. 897-902
Series
IFAC papers online, E-ISSN 2405-8963
Keywords [en]
Input selection; System identification; ARX-models; ARMAX-models; Signal-to-noise ratio
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-152415DOI: 10.1016/j.ifacol.2018.09.080ISI: 000446599200152OAI: oai:DiVA.org:liu-152415DiVA, id: diva2:1259586
Conference
18th IFAC Symposium on System Identification (SYSID)
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-30

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Klingspor, MånsHansson, AndersLöfberg, Johan
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  • apa
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  • vancouver
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  • Other style
More styles
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  • de-DE
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  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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