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Identification of Gaussian process state-space models with particle stochastic approximation EM
Department of Engineering, University of Cambridge, UK.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Deptartment of Information Technology, Uppsala University, Sweden.
Department of Engineering, University of Cambridge, UK.
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014Conference paper, Published paper (Refereed)
Abstract [en]

Gaussian process state-space models (GP-SSMs) are a very exible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We present an approach to maximum likelihood identification of the parameters in GP-SSMs, while retaining the full nonparametric description of the dynamics. The method is based on a stochastic approximation version of the EM algorithm that employs recent developments in particle Markov chain Monte Carlo for efficient identification.

Place, publisher, year, edition, pages
2014.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-110058OAI: oai:DiVA.org:liu-110058DiVA: diva2:742563
Conference
19th IFAC World Congress, Cape Town, South Africa, August 24-29, 2014.
Projects
CADICS
Available from: 2014-09-02 Created: 2014-09-02 Last updated: 2014-12-17

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fulltext(719 kB)204 downloads
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Lindsten, Fredrik

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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