A filtering based multi-innovation gradient estimation algorithm and performance analysis for nonlinear dynamical systems. (6th November 2017) - Wang, Yanjiao; Ding, Feng
Abstract: This article studies the problem for parameter identification of nonlinear dynamical systems (i.e., the Hammerstein–Wiener systems) with additive coloured noises. Based on the gradient search and the key term separation, a generalized extended stochastic gradient (GESG) algorithm is given for estimating the system parameters. To improve the computational efficiency, a data filtering based GESG algorithm and a data filtering based multi-innovation GESG algorithm are derived by applying the data filtering technique and the multi-innovation identification theory. Moreover, the proposed algorithms are proved to be convergent under proper conditions. Finally, the simulation results verify the theoretical analysis.