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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
Abstract
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.
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