Input selection in N2SID using group lasso regularization

M. Klingspor, A Hansson, J. Löfberg, Michel Verhaegen

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22 Downloads (Pure)

Abstract

Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.
Original languageEnglish
Title of host publicationIFAC-PapersOnLine
Subtitle of host publicationProceedings 20th IFAC World Congress
EditorsDenis Dochain, Didier Henrion, Dimitri Peaucelle
Place of PublicationLaxenburg, Austria
PublisherElsevier
Pages9474-9479
Volume50-1
DOIs
Publication statusPublished - 2017
Event20th World Congress of the International Federation of Automatic Control (IFAC), 2017 - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
https://www.ifac2017.org

Publication series

NameIFAC-PapersOnline
Number1
Volume50
ISSN (Print)2405-8963

Conference

Conference20th World Congress of the International Federation of Automatic Control (IFAC), 2017
Abbreviated titleIFAC 2017
CountryFrance
CityToulouse
Period9/07/1714/07/17
Internet address

Keywords

  • Input selection
  • System identification
  • State-space models
  • N2SID
  • Subspace methods
  • Signal-to-noise ratio

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  • Cite this

    Klingspor, M., Hansson, A., Löfberg, J., & Verhaegen, M. (2017). Input selection in N2SID using group lasso regularization. In D. Dochain, D. Henrion, & D. Peaucelle (Eds.), IFAC-PapersOnLine: Proceedings 20th IFAC World Congress (Vol. 50-1, pp. 9474-9479). (IFAC-PapersOnline; Vol. 50, No. 1). Elsevier. https://doi.org/10.1016/j.ifacol.2017.08.1472