Recursive least squares algorithm and gradient algorithm for Hammerstein-Wiener systems using the data filtering

被引：73
|
作者：
Wang, Yanjiao [1 ]
Ding, Feng [1 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
来源：
NONLINEAR DYNAMICS | 2016年 / 84卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Least squares; Gradient search; Data filtering; Nonlinear system; PARAMETER-ESTIMATION ALGORITHM; NONLINEAR-SYSTEMS; DYNAMIC-SYSTEMS; IDENTIFICATION; BACKLASH; DESIGN;
D O I：
10.1007/s11071-015-2548-5
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
This paper considers the parameter estimation problems of Hammerstein-Wiener systems by using the data filtering technique. In order to improve the estimation accuracy, the data filtering-based recursive generalized extended least squares algorithm is derived. In order to improve the computational efficiency, the data filtering-based generalized extended stochastic gradient algorithm is derived for estimating the system parameters. Finally, the computational efficiency of the proposed algorithms is analyzed and compared. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of Hammerstein-Wiener systems.
引用
收藏
页码：1045 / 1053
页数：9
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