Principal Component Analysis for Errors-In-Variables Subspace Identification

Jin WANG; S. Joe QIN

doi:10.1109/cdc.2001.980491

Principal Component Analysis for Errors-In-Variables Subspace Identification

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

This paper develops a new subspace identification algorithm using principal component analysis (PCA) that gives consistent model estimates under the errors-in-variables (EIV) situation. PCA naturally falls into the category of EIV formulation, which resembles total least squares and allows for errors in both process input and output. We propose to use PCA to determine the A, B, C, and D matrices and the system order for an EIV formulation. Standard PCA is modified with instrumental variables in order to achieve consistent estimates of the system matrices. The proposed subspace identification method is demonstrated using one simulated processe and a real industrial process for model identification and order determination.

Original language	English
Title of host publication	Proceedings of the 40th IEEE Conference on Decision and Control
Publisher	Institute of Electrical and Electronics Engineers
Pages	3936-3941
Number of pages	6
ISBN (Electronic)	0780370635
ISBN (Print)	0780370619
DOIs	https://doi.org/10.1109/cdc.2001.980491
Publication status	Published - Dec 2001
Externally published	Yes
Event	40th IEEE Conference on Decision and Control (CDC) - Orlando, United States Duration: 4 Dec 2001 → 7 Dec 2001

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Publisher	Institute of Electrical and Electronics Engineers
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	40th IEEE Conference on Decision and Control (CDC)
Country/Territory	United States
City	Orlando
Period	4/12/01 → 7/12/01

Access to Document

10.1109/cdc.2001.980491

Cite this

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title = "Principal Component Analysis for Errors-In-Variables Subspace Identification",

abstract = "This paper develops a new subspace identification algorithm using principal component analysis (PCA) that gives consistent model estimates under the errors-in-variables (EIV) situation. PCA naturally falls into the category of EIV formulation, which resembles total least squares and allows for errors in both process input and output. We propose to use PCA to determine the A, B, C, and D matrices and the system order for an EIV formulation. Standard PCA is modified with instrumental variables in order to achieve consistent estimates of the system matrices. The proposed subspace identification method is demonstrated using one simulated processe and a real industrial process for model identification and order determination.",

author = "Jin WANG and QIN, {S. Joe}",

year = "2001",

month = dec,

doi = "10.1109/cdc.2001.980491",

language = "English",

isbn = "0780370619",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers",

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booktitle = "Proceedings of the 40th IEEE Conference on Decision and Control",

note = "40th IEEE Conference on Decision and Control (CDC) ; Conference date: 04-12-2001 Through 07-12-2001",

}

WANG, J & QIN, SJ 2001, Principal Component Analysis for Errors-In-Variables Subspace Identification. in Proceedings of the 40th IEEE Conference on Decision and Control. Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers, pp. 3936-3941, 40th IEEE Conference on Decision and Control (CDC), Orlando, Florida, United States, 4/12/01. https://doi.org/10.1109/cdc.2001.980491

Principal Component Analysis for Errors-In-Variables Subspace Identification. / WANG, Jin; QIN, S. Joe.
Proceedings of the 40th IEEE Conference on Decision and Control. Institute of Electrical and Electronics Engineers, 2001. p. 3936-3941 (Proceedings of the IEEE Conference on Decision and Control).

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

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N2 - This paper develops a new subspace identification algorithm using principal component analysis (PCA) that gives consistent model estimates under the errors-in-variables (EIV) situation. PCA naturally falls into the category of EIV formulation, which resembles total least squares and allows for errors in both process input and output. We propose to use PCA to determine the A, B, C, and D matrices and the system order for an EIV formulation. Standard PCA is modified with instrumental variables in order to achieve consistent estimates of the system matrices. The proposed subspace identification method is demonstrated using one simulated processe and a real industrial process for model identification and order determination.

AB - This paper develops a new subspace identification algorithm using principal component analysis (PCA) that gives consistent model estimates under the errors-in-variables (EIV) situation. PCA naturally falls into the category of EIV formulation, which resembles total least squares and allows for errors in both process input and output. We propose to use PCA to determine the A, B, C, and D matrices and the system order for an EIV formulation. Standard PCA is modified with instrumental variables in order to achieve consistent estimates of the system matrices. The proposed subspace identification method is demonstrated using one simulated processe and a real industrial process for model identification and order determination.

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DO - 10.1109/cdc.2001.980491

M3 - Conference paper (refereed)

SN - 0780370619

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 3936

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BT - Proceedings of the 40th IEEE Conference on Decision and Control

PB - Institute of Electrical and Electronics Engineers

T2 - 40th IEEE Conference on Decision and Control (CDC)

Y2 - 4 December 2001 through 7 December 2001

ER -

Principal Component Analysis for Errors-In-Variables Subspace Identification

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