Adaptive filtering parameter estimation algorithms for Hammerstein nonlinear systems

被引：21

作者：

Mao, Yawen ^{[1
,2
]}

Ding, Feng ^{[1
,3
]}

Alsaedi, Ahmed ^{[3
]}

Hayat, Tasawar ^{[3
,4
]}

机构：

[1] Jiangnan Univ, Sch Internet Things Engn, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China

[2] King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia

[3] King Abdulaziz Univ, Dept Math, Nonlinear Anal & Appl Math NAAM Res Grp, Jeddah 21589, Saudi Arabia

[4] Quaid I Azam Univ, Dept Math, Islamabad 44000, Pakistan

来源：

SIGNAL PROCESSING | 2016年 / 128卷

基金：

中国国家自然科学基金;

关键词：

Parameter estimation; Recursive identification; Nonlinear system; Adaptive filtering; Multi-innovation identification theory; STOCHASTIC GRADIENT ALGORITHM; IDENTIFICATION ALGORITHM; RECURSIVE-IDENTIFICATION; STRATEGY;

D O I：

10.1016/j.sigpro.2016.05.009

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper studies the parameter estimation problems of the Hammerstein nonlinear systems using the adaptive filtering technique. A linear filter based recursive least squares (LF-RLS) identification algorithm with good convergence properties and high parameter estimation accuracy is proposed by filtering the input-output data. A linear filter based multi-innovation stochastic gradient (LF-MISG) algorithm is proposed by the innovation expansion, in order to improve the computational efficiency of the LF-RLS algorithm. Furthermore, a time-varying factor is introduced in the linear filter to improve the convergence speed of the LF-MISG algorithm. The efficiency of the proposed algorithms are shown in comparison with the conventional identification algorithms. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：417 / 425

页数：9

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