Principal Component Analysis for Errors-In-Variables Subspace Identification

Jin WANG, S. Joe QIN*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

10 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 languageEnglish
Title of host publicationProceedings of the 40th IEEE Conference on Decision and Control
PublisherInstitute of Electrical and Electronics Engineers
Pages3936-3941
Number of pages6
ISBN (Electronic)0780370635
ISBN (Print)0780370619
DOIs
Publication statusPublished - Dec 2001
Externally publishedYes
Event40th IEEE Conference on Decision and Control (CDC) - Orlando, United States
Duration: 4 Dec 20017 Dec 2001

Publication series

NameProceedings of the IEEE Conference on Decision and Control
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference40th IEEE Conference on Decision and Control (CDC)
Country/TerritoryUnited States
CityOrlando
Period4/12/017/12/01

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