Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle

被引：64

作者：

Chen, Huibo ^{[1
]}

Xiao, Yongsong ^{[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：

APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 247卷

基金：

中国国家自然科学基金;

关键词：

Stochastic gradient; Parameter estimation; Hierarchical identification; Auxiliary model; Hammerstein system; MODEL-PREDICTIVE CONTROL; LEAST-SQUARES ALGORITHM; ITERATIVE ALGORITHM; LINEAR-SYSTEMS; IDENTIFICATION;

D O I：

10.1016/j.amc.2014.09.070

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we use the hierarchical identification principle to decompose a Hammerstein controlled autoregressive system into three subsystems, apply the key term separation principle to express the system output as a linear combination of the system parameters, and then derive a hierarchical gradient parameter estimation algorithm for identifying all subsystems. Finally, a multi-innovation stochastic gradient algorithm is presented to improve the estimation accuracy by making full of the identification innovation. The simulation results show that the proposed algorithm is effective. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：1202 / 1210

页数：9

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