Bias compensation based recursive least squares identification for equation error models with colored noises

Ai Guo WU; Fan YANG; Yang-Yang QIAN

doi:10.1109/ChiCC.2014.6896104

Bias compensation based recursive least squares identification for equation error models with colored noises

Ai Guo WU^*
, Fan YANG
, Yang-Yang QIAN

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

2 Citations (Scopus)

Abstract

It is well known that the least squares estimation of ARMAX models is biased. In this paper, by combining the principle of bias compensation and hierarchical identification, a new identification is established for this equation error model with moving average noises. The proposed estimate of the system parameter is given by the least squares estimate modified by a correction term. A numerical example is employed to show the advantage of the proposed estimation algorithm.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control Conference, CCC 2014
Editors	Shengyuan XU, Qianchuan ZHAO
Publisher	IEEE
Pages	6715-6720
Number of pages	6
ISBN (Electronic)	9789881563842
DOIs	https://doi.org/10.1109/ChiCC.2014.6896104
Publication status	Published - 11 Sept 2014
Externally published	Yes
Event	33rd Chinese Control Conference, CCC 2014 - Nanjing, China Duration: 28 Jul 2014 → 30 Jul 2014

Publication series

Name	Proceedings of the Chinese Control Conference (CCC)
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	33rd Chinese Control Conference, CCC 2014
Country/Territory	China
City	Nanjing
Period	28/07/14 → 30/07/14

Bibliographical note

Publisher Copyright:
© 2014 TCCT, CAA.

Keywords

Bias compensation
Covariance
Least squares estimation

Access to Document

10.1109/ChiCC.2014.6896104

Cite this

WU, A. G., YANG, F., & QIAN, Y.-Y. (2014). Bias compensation based recursive least squares identification for equation error models with colored noises. In S. XU, & Q. ZHAO (Eds.), Proceedings of the 33rd Chinese Control Conference, CCC 2014 (pp. 6715-6720). Article 6896104 (Proceedings of the Chinese Control Conference (CCC)). IEEE. https://doi.org/10.1109/ChiCC.2014.6896104

@inproceedings{cf58c735cd2a4be48d3c5d12f50c7944,

title = "Bias compensation based recursive least squares identification for equation error models with colored noises",

abstract = "It is well known that the least squares estimation of ARMAX models is biased. In this paper, by combining the principle of bias compensation and hierarchical identification, a new identification is established for this equation error model with moving average noises. The proposed estimate of the system parameter is given by the least squares estimate modified by a correction term. A numerical example is employed to show the advantage of the proposed estimation algorithm.",

keywords = "Bias compensation, Covariance, Least squares estimation",

author = "WU, \{Ai Guo\} and Fan YANG and Yang-Yang QIAN",

note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",

year = "2014",

month = sep,

day = "11",

doi = "10.1109/ChiCC.2014.6896104",

language = "English",

series = "Proceedings of the Chinese Control Conference (CCC)",

publisher = "IEEE",

pages = "6715--6720",

editor = "Shengyuan XU and Qianchuan ZHAO",

booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",

}

WU, AG, YANG, F & QIAN, Y-Y 2014, Bias compensation based recursive least squares identification for equation error models with colored noises. in S XU & Q ZHAO (eds), Proceedings of the 33rd Chinese Control Conference, CCC 2014., 6896104, Proceedings of the Chinese Control Conference (CCC), IEEE, pp. 6715-6720, 33rd Chinese Control Conference, CCC 2014, Nanjing, China, 28/07/14. https://doi.org/10.1109/ChiCC.2014.6896104

Bias compensation based recursive least squares identification for equation error models with colored noises. / WU, Ai Guo; YANG, Fan; QIAN, Yang-Yang.
Proceedings of the 33rd Chinese Control Conference, CCC 2014. ed. / Shengyuan XU; Qianchuan ZHAO. IEEE, 2014. p. 6715-6720 6896104 (Proceedings of the Chinese Control Conference (CCC)).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

TY - GEN

T1 - Bias compensation based recursive least squares identification for equation error models with colored noises

AU - WU, Ai Guo

AU - YANG, Fan

AU - QIAN, Yang-Yang

PY - 2014/9/11

Y1 - 2014/9/11

N2 - It is well known that the least squares estimation of ARMAX models is biased. In this paper, by combining the principle of bias compensation and hierarchical identification, a new identification is established for this equation error model with moving average noises. The proposed estimate of the system parameter is given by the least squares estimate modified by a correction term. A numerical example is employed to show the advantage of the proposed estimation algorithm.

AB - It is well known that the least squares estimation of ARMAX models is biased. In this paper, by combining the principle of bias compensation and hierarchical identification, a new identification is established for this equation error model with moving average noises. The proposed estimate of the system parameter is given by the least squares estimate modified by a correction term. A numerical example is employed to show the advantage of the proposed estimation algorithm.

KW - Bias compensation

KW - Covariance

KW - Least squares estimation

UR - https://www.scopus.com/pages/publications/84907919812

U2 - 10.1109/ChiCC.2014.6896104

DO - 10.1109/ChiCC.2014.6896104

M3 - Conference paper (refereed)

AN - SCOPUS:84907919812

T3 - Proceedings of the Chinese Control Conference (CCC)

SP - 6715

EP - 6720

BT - Proceedings of the 33rd Chinese Control Conference, CCC 2014

A2 - XU, Shengyuan

A2 - ZHAO, Qianchuan

PB - IEEE

T2 - 33rd Chinese Control Conference, CCC 2014

Y2 - 28 July 2014 through 30 July 2014

ER -

Bias compensation based recursive least squares identification for equation error models with colored noises

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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