Stochastic gradient identification of polynomial Wiener systems: Analysis and application

This paper presents analytical, numerical, and experimental results for a s tochastic gradient adaptive scheme th:at identifies a polynomial-type nonli near system with memory for noisy output observations, The analysis include s the computation of the stationary points, the mean square error surface, and the stability regions of the algorithm for Gaussian data, Convergence o f the mean is studied using L-2 and Euclidian norms, Monte Cal lo simulatio ns confirm the theoretical predictions that show a small sensitivity to the observation noise. An application is presented for the identification of a nonlinear time-delayed feedback system.