Least squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data

被引：113

作者：

Liu, Yanjun ^{[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：

DIGITAL SIGNAL PROCESSING | 2010年 / 20卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Jacobi iterations; Gauss-Seidel iterations; Recursive identification; Parameter estimation; Least squares; Box-Jenkins models; IDENTIFICATION;

D O I：

10.1016/j.dsp.2010.01.004

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A least squares based iterative identification algorithm is developed for Box-Jenkins models (or systems). The proposed iterative algorithm can produce highly accurate parameter estimation compared with recursive approaches. The basic idea of the proposed iterative method is to adopt the interactive estimation theory: the parameter estimates relying on unknown variables are computed by using the estimates of these unknown variables which are obtained from the preceding parameter estimates. The numerical example indicates that the proposed iterative algorithm has fast convergence rates compared with the gradient based iterative algorithm. (C) 2010 Elsevier Inc. All rights reserved.

引用

页码：1458 / 1467

页数：10

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