Discrete-time nonlinear system identification using recurrent neural networks
Wen Yu, Xiaoou Li · 2004
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/sub /spl infin// and robust to any bounded uncertainties.