In general, neural networks cannot match nonlinear systems exactly. Neuro identifier has to include robust modification in order to guarantee Lyapunov stability. In this paper input-to-state stability approach is applied to access robust training algorithms of discrete-time recurrent neural networks. We conclude that for 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.
['Wen Yu']
Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms