liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
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
System Identification Via Sparse Multiple Kernel-Based Regularization Using Sequential Convex Optimization Techniques
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-8655-2655
Technical University of Denmark, Denmark.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Padua, Italy.
Show others and affiliations
2014 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 11, 2933-2945 p.Article in journal (Refereed) Published
Abstract [en]

Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels suitable for impulse response estimation, and equip the kernel-based regularization method with three features. First, multiple kernels can better capture complicated dynamics than single kernels. Second, the estimation of their weights by maximizing the marginal likelihood favors sparse optimal weights, which enables this method to tackle various structure detection problems, e. g., the sparse dynamic network identification and the segmentation of linear systems. Third, the marginal likelihood maximization problem is a difference of convex programming problem. It is thus possible to find a locally optimal solution efficiently by using a majorization minimization algorithm and an interior point method where the cost of a single interior-point iteration grows linearly in the number of fixed kernels. Monte Carlo simulations show that the locally optimal solutions lead to good performance for randomly generated starting points.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2014. Vol. 59, no 11, 2933-2945 p.
Keyword [en]
System identification; regularization; kernel; convex optimization; sparsity; structure detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-112818DOI: 10.1109/TAC.2014.2351851ISI: 000344482500007OAI: oai:DiVA.org:liu-112818DiVA: diva2:777075
Note

Funding Agencies|Linnaeus Center CADICS - Swedish Research Council; ERC advanced grant LEARN [267381]; ERC - European Research Council [291405]; MIUR FIRB project "Learning meets time" [RBFR12M3AC]; European Community [257462]

Available from: 2015-01-08 Created: 2014-12-17 Last updated: 2017-12-05

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Chen, TianshiLjung, Lennart

Search in DiVA

By author/editor
Chen, TianshiLjung, Lennart
By organisation
Automatic ControlThe Institute of Technology
In the same journal
IEEE Transactions on Automatic Control
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 644 hits
CiteExportLink to record
Permanent link

Direct link
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