Maximum likelihood recursive least squares estimation for multivariate equation-error ARMA systems

被引：10

作者：

Liu, Lijuan ^{[1
]}

Wang, Yan ^{[1
]}

Wang, Cheng ^{[1
]}

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

Hayat, Tasawar ^{[3
]}

机构：

[1] Jiangnan Univ, Sch Internet Things Engn, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China

[2] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266061, Peoples R China

[3] King Abdulaziz Univ, Fac Sci, Dept Math, Nonlinear Anal & Appl Math NAAM Res Grp, Jeddah 21589, Saudi Arabia

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2018年 / 355卷 / 15期

关键词：

PARAMETER-ESTIMATION ALGORITHM; THRESHOLD DIVIDEND STRATEGY; MOVING AVERAGE NOISE; IDENTIFICATION ALGORITHMS; DYNAMICAL-SYSTEMS; MULTI-INNOVATION; MODEL; PERFORMANCE; JUMP;

D O I：

10.1016/j.jfranklin.2018.07.041

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 focuses on the parameter estimation problems of multivariate equation-error systems. A recursive generalized extended least squares algorithm is presented as a comparison. Based on the maximum likelihood principle and the coupling identification concept, the multivariate equation-error system is decomposed into several regressive identification models, each of which has only a parameter vector, and a coupled subsystem maximum likelihood recursive least squares identification algorithm is developed for estimating the parameter vectors of these submodels. The simulation example shows that the proposed algorithm is effective and has high estimation accuracy. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：7609 / 7625

页数：17

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