Identification of state-space systems using a dual time-frequency domain approach

被引：2
|
作者：
Agueero, Juan C. [1 ]
Yuz, Juan I. [2 ]
Goodwin, Graham C. [1 ]
Tang, Wei [3 ]
机构：
[1] Univ Newcastle, Callaghan, NSW 2308, Australia
[2] Univ Tecn Federico Santa Maria, Valparaiso, Chile
[3] Northwestern Polytech Univ, Xian, Peoples R China
来源：
49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2010年
关键词：
MAXIMUM-LIKELIHOOD-ESTIMATION; MODELS;
D O I：
10.1109/CDC.2010.5717056
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 state-space models by using a dual time-frequency domain approach. We propose an Expectation Maximization formulation that considers a (non-bijective) 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 also explore the application of these ideas to Errors-In-Variables systems.
引用
收藏
页码：2863 / 2868
页数：6
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