A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

被引：42

作者：

Mao, Yawen ^{[1
]}

Ding, Feng ^{[2
]}

机构：

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

[2] Jiangnan Univ, Control Sci & Engn Res Ctr, Wuxi 214122, Peoples R China

来源：

DIGITAL SIGNAL PROCESSING | 2015年 / 46卷

基金：

中国国家自然科学基金;

关键词：

Multi-innovation identification; Nonlinear system; Key-term separation principle; Data filtering technique; MOVING AVERAGE SYSTEMS; MODEL-PREDICTIVE CONTROL; PARAMETER-ESTIMATION; IDENTIFICATION ALGORITHM; PERFORMANCE ANALYSIS; ITERATIVE ALGORITHM; NEWTON ITERATION; DYNAMIC-SYSTEMS; WIENER SYSTEMS; COLORED NOISE;

D O I：

10.1016/j.dsp.2015.07.002

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：

The identification of nonlinear systems is a hot topic in the identification fields. In this paper, a data filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein nonlinear controlled autoregressive moving average systems by adopting the key-term separation principle and the data filtering technique. The proposed algorithm provides a reference to improve the identification accuracy of the nonlinear systems with colored noise. The simulation results show that the new algorithm can more effectively estimate the parameters of the Hammerstein nonlinear systems than the multi-innovation stochastic gradient algorithm. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：215 / 225

页数：11

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