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Robust High-Gain DNN Observer for Nonlinear Stochastic Continuous Time Systems
Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico.
2007 (English)Report (Other academic)
Abstract [en]

A class of nonlinear stochastic processes satysfying a "Lipschitz-type strip condition" and supplied by a linear output equation, is considered. Robust asymptotic (high-gain) state estimation for nonlinear stochastic processes via differential neural networks is discussed. A new type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis (with Ito formula implementation), the stability conditions for the state estimation error as well as for the neural network weights are established. The upper bound for this error is derived. The numerical example, dealing with "module"-type nonlinearities, illustrates the effectiveness of the suggested approach.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2007. , 9 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2803
Keyword [en]
Nonlinear observers, Dynamic neural networks, Stochastic processes
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-56129ISRN: LiTH-ISY-R-2803OAI: oai:DiVA.org:liu-56129DiVA: diva2:316970
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-02Bibliographically approved

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Ljung, Lennart

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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