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.
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页码：2763 / 2775
页数：13
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