Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory - Zhou, Yihong; Ma, Fengying; Ding, Feng; Xu, Ling; Alsaedi, Ahmed; Hayat, Tasawar (International journal of robust and nonlinear control, 2020)
Summary This article studies the parameter estimation problems of radial basis function‐based state‐dependent autoregressive models with autoregressive noises (RBF‐ARAR models). To reduce the effect of the colored noise to parameter estimation, the data filtering technique is applied and a filtering based generalized stochastic gradient algorithm is derived for the RBF‐ARAR models. In order to achieve more accurate parameter estimates, a filtering based multiinnovation generalized stochastic gradient (F‐MI‐GSG) algorithm is proposed by utilizing the current and past innovations. Introducing two forgetting factors, a filtering based multiinnovation generalized forgetting gradient algorithm is developed to improve the transient performance of the F‐MI‐GSG algorithm. The effectiveness of the proposed algorithms is verified through the simulation examples.