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Improving Linear State-Space Models with Additional NIterations
Math Works, MA 01760 USA.
Math Works, MA 01760 USA.
Chalmers Univ Technol, Sweden.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
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2018 (English)In: 18th IFAC Symposium on System Identification (SYSID), Proceedings, ELSEVIER SCIENCE BV , 2018, Vol. 51, no 15, p. 341-346Conference paper, Published paper (Refereed)
Abstract [en]

An estimated state-space model can possibly be improved by further iterations with estimation data. This contribution specifically studies if models obtained by subspace estimation can be improved by subsequent re-estimation of the B, C, and D matrices (which involves linear estimation problems). Several tests are performed, which show that it is generally advisable to do such further re-estimation steps using the maximum likelihood criterion. Stated more succinctly in terms of MATLABC (R) functions, ssest generally outperforms n4sid. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2018. Vol. 51, no 15, p. 341-346
Series
IFAC papers online, E-ISSN 2405-8963
Keywords [en]
Parameter estimation; State-space models; Subspace identification; Maximum Likelihood
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-152412DOI: 10.1016/j.ifacol.2018.09.158ISI: 000446599200059OAI: oai:DiVA.org:liu-152412DiVA, id: diva2:1259590
Conference
18th IFAC Symposium on System Identification (SYSID)
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-30

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  • apa
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