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Semi-parametric kernel-based identification of Wiener systems
Royal Institute of Technology, Stockholm, Sweden.
Uppsala University, Uppsala, Sweden.
KTH - Royal Institute of Technology, Stockholm, Sweden.
2018 (English)In: Proceedings of the 57th IEEE Conference on Decision and Control (CDC), IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

We present a technique for kernel-based identification of Wiener systems. We model the impulse response of the linear block with a Gaussian process. The static nonlinearity is modeled with a combination of basis functions. The coefficients of the static nonlinearity are estimated, together with the hyperparameters of the covariance function of the Gaussian process model, using an iterative algorithm based on the expectation-maximization method combined with elliptical slice sampling to sample from the posterior distribution of the impulse response given the data. The same sampling method is then used to find the posterior-mean estimate of the impulse response. We test the proposed algorithm on a benchmark of randomly-generated Wiener systems.

Place, publisher, year, edition, pages
IEEE, 2018.
Series
Conference on Decision and Control (CDC), ISSN 2576-2370
National Category
Probability Theory and Statistics Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-159496DOI: 10.1109/CDC.2018.8619482ISI: 000458114803094ISBN: 978-1-5386-1395-5 (electronic)OAI: oai:DiVA.org:liu-159496DiVA, id: diva2:1341651
Conference
57th IEEE Conference on Decision and Control (CDC), 17-19 Dec. 2018
Funder
Swedish Research Council, 2016-04278Swedish Foundation for Strategic Research, ICA16-0015Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2024-01-26

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Lindsten, Fredrik

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CiteExportLink to record
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Citation style
  • apa
  • 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