Dual time-frequency domain system identification

被引：31

作者：

Aguero, Juan C. ^{[1
]}

Tang, Wei

Yuz, Juan I. ^{[2
]}

Delgado, Ramon ^{[1
]}

Goodwin, Graham C. ^{[1
]}

机构：

[1] Univ Newcastle, Sch Elect Engn & Comp Sci, Callaghan, NSW 2308, Australia

[2] Univ Tecn Federico Santa Maria, Dept Elect Engn, Valparaiso, Chile

来源：

AUTOMATICA | 2012年 / 48卷 / 12期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

Robust system identification; Maximum likelihood; MAXIMUM-LIKELIHOOD-ESTIMATION; ROBUST IDENTIFICATION; STATE; MODELS; EM; CONVERGENCE; REGRESSION; MATRIX;

D O I：

10.1016/j.automatica.2012.08.033

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we obtain the maximum likelihood estimate of the parameters of discrete-time linear models by using a dual time-frequency domain approach. We propose a formulation that considers a (reduced-rank) linear transformation of the available data. Such a transformation may correspond to different options: selection of time-domain data, transformation to the frequency domain, or selection of frequency-domain data obtained from time-domain samples. We use the proposed approach to identify multivariate systems represented in state-space form by using the Expectation-Maximisation algorithm. We illustrate the benefits of the approach via numerical examples. (c) 2012 Elsevier Ltd. All rights reserved.

引用

页码：3031 / 3041

页数：11

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