Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle

被引：13

作者：

Chen, Feiyan ^{[1
]}

Ding, Feng ^{[1
,2
]}

Alsaedi, Ahmed ^{[2
]}

Hayat, Tasawar ^{[2
,3
]}

机构：

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

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

[3] Quaid I Azam Univ, Dept Math, Islamabad 44000, Pakistan

来源：

MATHEMATICS AND COMPUTERS IN SIMULATION | 2017年 / 132卷

基金：

中国国家自然科学基金;

关键词：

System identification; Maximum likelihood; Multi-innovation; Recursive estimation; Data filtering; SQUARES IDENTIFICATION ALGORITHM; PARAMETER-ESTIMATION; STOCHASTIC-SYSTEMS;

D O I：

10.1016/j.matcom.2016.06.006

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper combines the data filtering technique with the maximum likelihood principle for parameter estimation of controlled autoregressive ARMA (autoregressive moving average) systems. We use an estimated noise transfer function to filter the input-output data and derive a filtering based maximum likelihood multi-innovation extended gradient algorithm to estimate the parameters of the systems by replacing the unmeasurable variables in the information vectors with their estimates. A maximum likelihood generalized extended gradient algorithm is given for comparison. A numerical simulation is given to support the developed methods. (C) 2016 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

引用

页码：53 / 67

页数：15

共 46 条

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