Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence

被引：44
|
作者：
Ding, Feng [1 ,2 ]
Meng, Dandan [2 ]
Dai, Jiyang [1 ]
Li, Qishen [1 ]
Alsaedi, Ahmed [3 ]
Hayat, Tasawar [3 ,4 ]
机构：
[1] Nanchang Hangkong Univ, Minist Educ, Nondestruct Test Key Lab, Nanchang 330063, Jiangxi, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
[3] King Abdulaziz Univ, Fac Sci, Dept Math, NAAM Res Grp, Jeddah, Saudi Arabia
[4] Quaid I Azam Univ, Dept Math, Islamabad, Pakistan
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2018年 / 16卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Dynamical system; iterative method; least squares; model equivalence; parameter estimation; MARKOVIAN JUMP SYSTEMS; IDENTIFICATION ALGORITHMS; ERROR SYSTEMS; STATE; VARIABLES; DESIGN; DELAY;
D O I：
10.1007/s12555-017-0001-x
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
By means of the model equivalence theory, this paper proposes a model equivalence based least squares iterative algorithm for estimating the parameters of stochastic dynamical systems with ARMA noise. The proposed algorithm reduces the number of the unknown noise terms in the information vector and can give more accurate parameter estimates compared with the generalized extended least squares algorithm. The validity of the proposed method is evaluated through a numerical example.
引用
收藏
页码：630 / 639
页数：10
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