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Regularized LTI System Identification with Multiple Regularization Matrix
Chinese Univ Hong Kong, Peoples R China; Chinese Univ Hong Kong, Peoples R China.
Tech Univ Denmark, Denmark.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Chinese Univ Hong Kong, Peoples R China.
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2018 (English)In: 18th IFAC Symposium on System Identification (SYSID), Proceedings, ELSEVIER SCIENCE BV , 2018, Vol. 51, no 15, p. 180-185Conference paper, Published paper (Refereed)
Abstract [en]

Regularization methods with regularization matrix in quadratic form have received increasing attention. For those methods, the design and tuning of the regularization matrix are two key issues that are closely related. For systems with complicated dynamics, it would be preferable that the designed regularization matrix can bring the hyper-parameter estimation problem certain structure such that a locally optimal solution can be found efficiently. An example of this idea is to use the so-called multiple kernel Chen et al. (2014) for kernel-based regularization methods. In this paper, we propose to use the multiple regularization matrix for the filter-based regularization. Interestingly, the marginal likelihood maximization with the multiple regularization matrix is also a difference of convex programming problem, and a locally optimal solution could be found with sequential convex optimization techniques. (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. 180-185
Series
IFAC papers online, E-ISSN 2405-8963
Keywords [en]
System identification; regularization methods; sequential convex optimization
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-152410DOI: 10.1016/j.ifacol.2018.09.121ISI: 000446599200032OAI: oai:DiVA.org:liu-152410DiVA, id: diva2:1259593
Conference
18th IFAC Symposium on System Identification (SYSID)
Note

Funding Agencies|Thousand Youth Talents Plan of China; NSFC [61773329, 61603379]; Shenzhen Science and Technology Innovation Council [Ji-20170189, Ji-20160207]; Chinese University of Hong Kong, Shenzhen [2014.0003.23]; Swedish Research Council [2014-5894]; National Key Basic Research Program of China (973 Program) [2014CB845301]; AMSS, CAS [2015-hwyxqnrc-mbq]; [PF.01.000249]

Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-30

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf