Filtered generalized iterative parameter identification for equation-error autoregressive models based on the filtering identification idea

被引:72
作者
Ding, Feng [1 ,2 ]
Shao, Xingling [1 ]
Xu, Ling [3 ,4 ]
Zhang, Xiao [2 ]
Xu, Huan [3 ]
Zhou, Yihong [5 ]
机构
[1] North Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China
[2] Jiangnan Univ, Sch Internet Things Engn, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[3] Changzhou Univ, Sch Microelect & Control Engn, Changzhou, Peoples R China
[4] Hubei Univ Technol, Sch Elect & Elect Engn, Wuhan, Peoples R China
[5] Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
filtering identification; gradient search; iterative identification; least squares; multi-innovation identification; parameter estimation; stochastic system; ESTIMATION ALGORITHM; STOCHASTIC-SYSTEM; FAULT-DIAGNOSIS; NOISE; OPTIMIZATION; SELECTION; TRACKING; DELAY;
D O I
10.1002/acs.3753
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 using the collected batch data and the iterative search, and based on the filtering identification idea, this article investigates and proposes a filtered multi-innovation generalized projection-based iterative identification method, a filtered generalized gradient-based iterative identification method, a filtered generalized least squares-based iterative identification method, a filtered multi-innovation generalized gradient-based iterative identification method and a filtered multi-innovation generalized least squares-based iterative identification method for equation-error autoregressive systems described by the equation-error autoregressive models. These filtered generalized iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises.
引用
收藏
页码:1363 / 1385
页数:23
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