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On-Line Identification and Adaptive Trajectory Tracking for Nonlinear Stochastic Continuous Time Systems using Differential Neural Networks
CINVESTAV-IPN, Mexico.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2001 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 37, no 8, 1257-1268 p.Article in journal (Refereed) Published
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
Elsevier, 2001. Vol. 37, no 8, 1257-1268 p.
Keyword [en]
Adaptive control, Dynamic neural networks, Identification, Stochastic processes
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-47300DOI: 10.1016/S0005-1098(01)00067-XOAI: oai:DiVA.org:liu-47300DiVA: diva2:268196
Note

© 2001 Elsevier Science Ltd.

Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13

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Ljung, Lennart

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