Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems

被引：68

作者：

Chen, Jing ^{[1
,2
]}

Zhang, Yan ^{[3
]}

Ding, Ruifeng ^{[1
,4
]}

机构：

[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Peoples R China

[2] Wuxi Profess Coll Sci & Technol, Wuxi 214028, Peoples R China

[3] Wuxi Inst Technol, Wuxi 214121, Peoples R China

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

来源：

MATHEMATICAL AND COMPUTER MODELLING | 2010年 / 52卷 / 9-10期

基金：

中国国家自然科学基金;

关键词：

Parameter estimation; Stochastic gradient; Auxiliary model identification; Multi-innovation identification; Multi-input multi-output systems; SQUARES IDENTIFICATION METHODS; PARAMETER-ESTIMATION; PERFORMANCE ANALYSIS; OUTPUT ESTIMATION;

D O I：

10.1016/j.mcm.2010.05.026

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper considers the identification problem for multi-input multi-output nonlinear systems. The difficulty of the parameter identification of such systems is that the information vector in the identification model contains unknown variables. The solution is using the auxiliary model identification idea to overcome the difficulty. An auxiliary model based multi-innovation extended stochastic gradient algorithm is presented by expanding the innovation vector to an innovation matrix. The proposed algorithm uses not only the current innovation but also the past innovations at each recursion and thus the parameter estimation accuracy can be improved. The numerical example shows that the proposed algorithm is effective. (C) 2010 Elsevier Ltd. All rights reserved

引用

页码：1428 / 1434

页数：7

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