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Discrete-time nonlinear system identification using recurrent neural networks

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2 Author(s)
W. Yu ; Dept. de Control Automatico, CINVESTAV-IPN, Mexico City, Mexico ; X. Li

In this paper we proposed a novel discrete-time recurrent neural networks. Input-to-state stability (ISS) approach is applied to access robust training algorithms. We conclude that for discrete-time nonlinear system identification, the gradient descent law and the backpropagation-like algorithm for the weights adjustment are stable in the sense of L and robust to any bounded uncertainties.

Published in:

Decision and Control, 2003. Proceedings. 42nd IEEE Conference on  (Volume:4 )

Date of Conference:

9-12 Dec. 2003