Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling

被引：255
作者：
Ding, Feng [1 ,2 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Control Sci & Engn Res Ctr, Wuxi 214122, Peoples R China
来源：
APPLIED MATHEMATICAL MODELLING | 2013年 / 37卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Parameter estimation; Recursive identification; Least squares; Stochastic gradient; Nonlinear system; Multi-innovation identification; ERRORS-IN-VARIABLES; SQUARES IDENTIFICATION METHODS; PARAMETER-ESTIMATION; ACCURACY;
D O I：
10.1016/j.apm.2012.04.039
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper decomposes a Hammerstein nonlinear system into two subsystems, one containing the parameters of the linear dynamical block and the other containing the parameters of the nonlinear static block, and presents a hierarchical multi-innovation stochastic gradient identification algorithm for Hammerstein systems based on the hierarchical identification principle. The proposed algorithm is simple in principle and easy to implement on-line. A simulation example is provided to test the effectiveness of the proposed algorithm. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码：1694 / 1704
页数：11
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