Recursive identification for multivariate autoregressive equation-error systems with autoregressive noise

被引：5

作者：

Liu, Lijuan ^{[1
]}

Ding, Feng ^{[1
,2
,3
]}

Zhu, Quanmin ^{[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] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266042, Peoples R China

[3] King Abdulaziz Univ, Dept Elect & Comp Engn, Jeddah, Saudi Arabia

[4] Univ West England, Dept Engn Design & Math, Bristol, Avon, England

来源：

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 2018年 / 49卷 / 13期

关键词：

Recursive identification; multivariate system; maximum likelihood; recursive least squares; PARAMETER-ESTIMATION ALGORITHM; STATE-SPACE SYSTEM; STRATEGY;

D O I：

10.1080/00207721.2018.1511873

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers the recursive identification problems for a class of multivariate autoregressive equation-error systems with autoregressive noise. By decomposing the system into several regressive identification subsystems, a maximum likelihood recursive generalised least squares identification algorithm is proposed to identify the parameter vectors in each subsystem. In addition, a multivariate recursive generalised least squares algorithm is derived as a comparison. The numerical simulation results indicate that the maximum likelihood recursive generalised least squares algorithm can effectively estimate the parameters of the multivariate autoregressive equation-error autoregressive systems and get more accurate parameter estimates than the multivariate recursive generalised least squares algorithm.

引用

下载

页码：2763 / 2775

页数：13

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