Robust identification by dynamic neural networks using sliding mode learning

• Autorzy: Poznyak A. S. , Yu W. , Sanchez E. N. , Sira-Ramirez H.
• Języki publikacji: EN

Abstrakty

EN

The problem of identification of continuous, uncertain nonlinear systems in the presence of bounded disturbances is implemented using dynamic neural networks. The proposed neural identifier guarantees a bound for the state estimation error. This bound turns out to be a linear combination of internal and external uncertainty levels. The neural net weights are updated on-line by a learning algorithm based on the sliding mode technique. To the best of the authors' knowledge, such a learning scheme is proposed for dynamic neural networks for the first time. Numerical simulations illustrate its effectiveness, even for highly nonlinear systems in the presence of important disturbances.

Słowa kluczowe

EN

dynamic neural networks; sliding mode

PL

dynamiczne sieci neuronowe; sterowanie ślizgowe

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: Applied Mathematics and Computer Science
• Rocznik: 1998
• Tom: Vol. 8, no 1
• Strony: 135--144
• Opis fizyczny: Bibliogr. 16 poz., wykr.

Twórcy

autor

Poznyak A. S.

autor

Yu W.

autor

Sanchez E. N.

autor

Sira-Ramirez H.

• CINVESTAV-IPN, Seccion de Control Automatico, Av. IPN 2508, A. P. 14-740, Mexico D. F., 07000,