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Estimation of General Nonlinear State-Space Systems
University of Newcastle, Australia.
University of Newcastle, Australia.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2010 (English)In: Proceedings of the 49th IEEE Conference on Decision and Control, 2010, 6371-6376 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel approach to the estimation of a general class of dynamic nonlinear system models. The main contribution is the use of a tool from mathematical statistics, known as Fishers’ identity, to establish how so-called “particle smoothing” methods may be employed to compute gradients of maximum-likelihood and associated prediction error cost criteria.

Place, publisher, year, edition, pages
2010. 6371-6376 p.
Keyword [en]
Maximum likelihood estimation, State-space methods, Statistics
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-63594DOI: 10.1109/CDC.2010.5717378ISBN: 978-1-4244-7745-6 (print)OAI: oai:DiVA.org:liu-63594DiVA: diva2:380819
Conference
The 49th IEEE Conference on Decision and Control, Atlanta, GA, USA, 15-17 December, 2010
Projects
CADICS
Funder
Swedish Research CouncilSwedish Foundation for Strategic Research
Available from: 2010-12-22 Created: 2010-12-22 Last updated: 2013-09-23

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Schön, Thomas

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
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Output format
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