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Input selection in ARX model estimation using group lasso regularization
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- Klingspor, Måns (author)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
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- Hansson, Anders (author)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
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- Löfberg, Johan (author)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten
- (creator_code:org_t)
- ELSEVIER SCIENCE BV, 2018
- 2018
- English.
- In: 18th IFAC Symposium on System Identification (SYSID), Proceedings. - : ELSEVIER SCIENCE BV. ; , s. 897-902
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- Input selection; System identification; ARX-models; ARMAX-models; Signal-to-noise ratio
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- kon (subject category)
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