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Robust High-Gain DNN Observer for Nonlinear Stochastic Continuous Time Systems
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.
Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico.
2001 (English)In: Proceedings of the 40th IEEE Conference on Decision and Control, 2001, 3546-3551 vol.4 p.Conference paper, Published paper (Refereed)
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
2001. 3546-3551 vol.4 p.
Keyword [en]
Nonlinear observers, Dynamic neural networks, Stochastic processes
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-90813DOI: 10.1109/.2001.980409ISBN: 0-7803-7061-9 (print)OAI: oai:DiVA.org:liu-90813DiVA: diva2:616353
Conference
40th IEEE Conference on Decision and Control, Orlando, FL, USA, December, 2001
Available from: 2013-04-16 Created: 2013-04-07 Last updated: 2013-04-16

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Ljung, Lennart

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  • nn-NO
  • nn-NB
  • sv-SE
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