Gradient based and least-squares based iterative identification methods for OE and OEMA systems

被引：201

作者：

Ding, Feng ^{[1
]}

Liu, Peter X. ^{[2
]}

Liu, Guangjun ^{[3
]}

机构：

[1] Jiangnan Univ, Sch Commun & Control Engn, Wuxi 214122, Peoples R China

[2] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada

[3] Ryerson Univ, Dept Aerosp Engn, Toronto, ON M5B 2K3, Canada

来源：

DIGITAL SIGNAL PROCESSING | 2010年 / 20卷 / 03期

基金：

加拿大自然科学与工程研究理事会; 中国国家自然科学基金;

关键词：

Recursive identification; Parameter estimation; Least squares; Stochastic gradient; Iterative algorithms; Missing data; Output error (OE) models; Output error moving average (OEMA) models; PARAMETER-ESTIMATION; ESTIMATION ALGORITHMS; PERFORMANCE ANALYSIS;

D O I：

10.1016/j.dsp.2009.10.012

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：

Gradient based and least-squares based iterative identification algorithms are developed for output error (OE) and output error moving average (OEMA) systems. Compared with recursive approaches, the proposed iterative algorithms use all the measured input-output data at each iterative computation (at each iteration), and thus can produce highly accurate parameter estimation. The basic idea of the iterative methods 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 simulation results confirm theoretical findings. (C) 2009 Elsevier Inc. All rights reserved.

引用

页码：664 / 677

页数：14

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