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
Sparse multiple kernels for impulse response estimation with majorization minimization algorithms
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-8655-2655
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
University of Padova, Italy.
Show others and affiliations
2012 (English)In: Decision and Control (CDC), 2012, IEEE , 2012, 1500-1505 p.Conference paper, Published paper (Refereed)
Abstract [en]

This contribution aims to enrich the recently introduced kernel-based regularization method for linear system identification. Instead of a single kernel, we use multiple kernels, which can be instances of any existing kernels for the impulse response estimation of linear systems. We also introduce a new class of kernels constructed based on output error (OE) model estimates. In this way, a more flexible and richer representation of the kernel is obtained. Due to this representation the associated hyper-parameter estimation problem has two good features. First, it is a difference of convex functions programming (DCP) problem. While it is still nonconvex, it can be transformed into a sequence of convex optimization problems with majorization minimization (MM) algorithms and a local minima can thus be found iteratively. Second, it leads to sparse hyper-parameters and thus sparse multiple kernels. This feature shows the kernel-based regularization method with multiple kernels has the potential to tackle various problems of finding sparse solutions in linear system identification.

Place, publisher, year, edition, pages
IEEE , 2012. 1500-1505 p.
Keyword [en]
Identification, Selection
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-103267DOI: 10.1109/CDC.2012.6426801ISI: 000327200401142ISBN: 978-1-4673-2065-8 (print)ISBN: 978-1-4673-2064-1 (print)OAI: oai:DiVA.org:liu-103267DiVA: diva2:688527
Conference
2012 IEEE 51st Annual Conference on Decision and Control (CDC), 10-13 December 2012, Maui, HI, USA
Available from: 2014-01-17 Created: 2014-01-16 Last updated: 2016-01-11Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Chen, TianshiLjung, LennartAndersen, Martin

Search in DiVA

By author/editor
Chen, TianshiLjung, LennartAndersen, Martin
By organisation
Automatic ControlThe Institute of Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 263 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