The residual-based ESG algorithm and its performance analysis

被引：18
|
作者：
Xiao, Yongsong [1 ]
Wang, Dongqing [2 ]
Ding, Feng [1 ]
机构：
[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Peoples R China
[2] Qingdao Univ, Coll Automat Engn, Qingdao 266071, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2010年 / 347卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Recursive identification; Parameter estimation; Convergence properties; Stochastic gradient; Stochastic approximation; STOCHASTIC GRADIENT ALGORITHMS; IDENTIFICATION ALGORITHMS; SYSTEMS; CONVERGENCE; APPROXIMATION; CONSISTENCY; MODELS;
D O I：
10.1016/j.jfranklin.2009.05.008
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The performance of the residual-based extended stochastic gradient (ESG) algorithms for identifying CARMA models with disturbances is analyzed under weaker conditions on statistical properties of the noise. The paper derives the conditions under which the parameter estimation errors converge to zero. Three examples are given to show the advantages of the proposed algorithm. (C) 2009 Published by Elsevier Ltd. on behalf of The Franklin Institute.
引用
收藏
页码：426 / 437
页数：12
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