Parameter Estimation of Neuro-Fuzzy Wiener Model With Colored Noise Using Separable Signals

被引：1
作者：
Lyu, Bensheng [1 ]
Jia, Li [1 ]
Li, Feng [2 ]
机构：
[1] Shanghai Univ, Coll Mechatron Engn & Automat, Dept Automat, Shanghai 200072, Peoples R China
[2] Jiangsu Univ Technol, Coll Elect & Informat Engn, Changzhou 213001, Jiangsu, Peoples R China
来源：
IEEE ACCESS | 2020年 / 8卷
基金：
中国国家自然科学基金;
关键词：
Autoregressive processes; Colored noise; Stochastic processes; Iterative methods; Biological system modeling; Computational modeling; Heuristic algorithms; Wiener model; separable signal; correlation analysis; stochastic gradient; IDENTIFICATION; SYSTEMS;
D O I：
10.1109/ACCESS.2020.2983969
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper considers a neuro-fuzzy based identification problem for Wiener model with controlled autoregressive moving average noise. The separable signal is applied to decouple the dynamic linear part and the static nonlinear part, and the correlation analysis method is adopted to estimate the parameters of the linear part. To improve the convergence rate of generalized extended stochastic gradient (GESG) algorithm, a generalized extended stochastic gradient algorithm with a forgetting factor is derived for estimating the parameters of the nonlinear part and the parameters of noise model. Examples results verify the effectiveness of the proposed method.
引用
收藏
页码：67047 / 67058
页数：12
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