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On-Line Identification and Adaptive Trajectory Tracking for Nonlinear Stochastic Continuous Time Systems using Differential Neural Networks
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2001 (English)Report (Other academic)
Abstract [en]

Identification of nonlinear stochastic processes via differential neural networks is discussed. A new "dead-zone" type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis the stability conditions for the identification error as well as for the neural network weights are established. The adaptive trajectory tracking using the obtained neural network model is realized for the subclass of stochastic completely controllable processes linearly dependent on control. The upper bounds for the identification and adaptive tracking errors are established.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2001. , 20 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2364
Keyword [en]
Adaptive control, Dynamic neural networks, Identification, Stochastic processes
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55841ISRN: LiTH-ISY-R-2364OAI: oai:DiVA.org:liu-55841DiVA: diva2:316695
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-04Bibliographically approved

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Ljung, Lennart

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • 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