International Journal of applied mathematics and computer science

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Paper details

Number 1 - March 1998
Volume 8 - 1998

Robust identification by dynamic neural networks using sliding mode learning

Alexander S. Poznyak, Wen Yu, Edgar N. Sanchez, Hebertt Sira-Ramirez

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.