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
收藏
页码：3031 / 3041
页数：11
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