A New Method for Identifying Errors-In-Variables System Models

被引：0

作者：

Kang, Hyundeok ^{[1
]}

Gu, Guoxiang ^{[1
]}

Zheng, Wei Xing ^{[2
]}

机构：

[1] Louisiana State Univ, Sch Elect Engn & Comp Sci, Baton Rouge, LA 70803 USA

[2] Western Sydney Univ, Sch Comp Engn & Math, Sydney, NSW 2751, Australia

来源：

PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC) | 2019年

基金：

中国国家自然科学基金;

关键词：

EIV model; Frisch scheme; subspace; system identification; total least-squares; BLIND IDENTIFICATION; ACCURACY ANALYSIS; FRISCH SCHEME; SUBSPACE;

D O I：

10.23919/chicc.2019.8865846

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We study the problem of system identification for the errors-in-variables (EIV) model, based on noisy input and output measurement data, without assuming equal noise variances at the system input and output. A new method is proposed and a new identification algorithm is developed based on the subspace interpretation. The algorithm is unbiased, and achieves the consistency of the parameter estimates. The performance of the identification algorithm is examined by a numerical example, and compared with that of the Frisch scheme based algorithm.

引用

页码：1537 / 1542

页数：6

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