Partial Least Squares Regression for Recursive System Identification

S. Joe QIN*

*Corresponding author for this work

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

40 Citations (Scopus)

Abstract

Industrial processes usually involve a large number of variables, many of which vary in a correlated manner. To identify a process model which has correlated variables, an ordinary least squares approach demonstrates ill-conditioned problem and the resulting model is sensitive to changes in sampled data. In this paper, a recursive partial least squares (PLS) regression is used for on-line system identification and circumventing the ill-conditioned problem. The partial least squares method is used to remove the correlation by projecting the original variable space to an orthogonal latent space. Applications of the proposed algorithm to a chemical process modeling problem is discussed.
Original languageEnglish
Title of host publicationProceedings of 32nd IEEE Conference on Decision and Control
PublisherInstitute of Electrical and Electronics Engineers
Pages2617-2621
Number of pages5
ISBN (Print)0780312988
DOIs
Publication statusPublished - Dec 1993
Externally publishedYes
Event32nd IEEE Conference on Decision and Control - San Antonio, United States
Duration: 15 Dec 199317 Dec 1993

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

Conference32nd IEEE Conference on Decision and Control
Country/TerritoryUnited States
CitySan Antonio
Period15/12/9317/12/93

Fingerprint

Dive into the research topics of 'Partial Least Squares Regression for Recursive System Identification'. Together they form a unique fingerprint.

Cite this