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Adaptive Forgetting in Recursive Identification through Multiple Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1984 (English)In: Analysis and Optimization of Systems: Proceedings of the Sixth International Conference on Analysis and Optimization of Systems, Nice, France, 19-22 June, 1984 / [ed] A. Bensoussan, J. L. Lions, New York: Springer Berlin/Heidelberg, 1984, 171-185 p.Chapter in book (Refereed)
Abstract [en]

A new recursive identification method, Adaptive Forgetting through Multiple Models — AFMM, is presented and evaluated using computer simulations. AFMM is especially suited for identification of systems with jumping parameters or parameters that change in an irregular fashion. It can be viewed as a particular way of implementing adaptive gains or adaptive forgetting factors for recursive identification. The new method essentialy consists of multiple Recursive Least Squares (RLS) algorithms running in parallel, each with a corresponding weighting factor. The simulations indicate that AFMM is able to track rapidly changing parameters well, and that the method is robust in several respects.

Place, publisher, year, edition, pages
New York: Springer Berlin/Heidelberg, 1984. 171-185 p.
Series
Lecture Notes in Control and Information Sciences, ISSN 0170-8643 ; 62
Keyword [en]
Recursive identification method, Adaptive Forgetting through Multiple Models (AFMM), Computer simulations, Recursive Least Squares (RLS), Algorithms, System analysis Congresses, Mathematical optimization Congresses, Automatic control Congresses, Biotechnology Congresses
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-102241DOI: 10.1007/BFb0004953ISBN: 0-387-13551-0 (print)ISBN: 3-540-13551-0 (print)ISBN: 3-540-13552-9 (print)ISBN: 0-387-13552-9 (print)OAI: oai:DiVA.org:liu-102241DiVA: diva2:675588
Available from: 2013-12-04 Created: 2013-12-04 Last updated: 2014-03-20

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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