Comparison between standard PLS, RLS, and unbiased PLS

A. Shamekh; B. Lennox; H. Lin

Comparison between standard PLS, RLS, and unbiased PLS

A. Shamekh, B. Lennox, H. Lin

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Partial Least Squares algorithm (PLS) is widely used in chemometric studies and increasingly in process engineering applications. For process problems, PLS is typically used as a dynamic modeling technique. Unfortunately, traditional PLS identification techniques will typically produce a model that is biased in its regression parameters. In this article, an effective unbiased recursive PLS algorithm is proposed to address this problem. This paper provides a comparative study, using simulated data, to illustrate the potential benefits of the proposed approach.

Original language	English
Title of host publication	Proceedings of the IASTED International Conference on Modelling, Identification and Control\|Proc. IASTED Int. Conf. Model. Ident. Control
Pages	327-332
Number of pages	5
Publication status	Published - 2010
Event	29th IASTED International Conference on Modelling, Identification and Control, MIC 2010 - Innsbruck Duration: 1 Jul 2010 → … http://www.actapress.com/Abstract.aspx?paperId=37799

Conference

Conference	29th IASTED International Conference on Modelling, Identification and Control, MIC 2010
City	Innsbruck
Period	1/07/10 → …
Internet address	http://www.actapress.com/Abstract.aspx?paperId=37799

Keywords

System identification, ordinary least squares, partial least squares, recursive identification.

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@inproceedings{619358f761344c1cbb7ce8525b5afac8,

title = "Comparison between standard PLS, RLS, and unbiased PLS",

abstract = "Partial Least Squares algorithm (PLS) is widely used in chemometric studies and increasingly in process engineering applications. For process problems, PLS is typically used as a dynamic modeling technique. Unfortunately, traditional PLS identification techniques will typically produce a model that is biased in its regression parameters. In this article, an effective unbiased recursive PLS algorithm is proposed to address this problem. This paper provides a comparative study, using simulated data, to illustrate the potential benefits of the proposed approach.",

keywords = "System identification, ordinary least squares, partial least squares, recursive identification.",

author = "A. Shamekh and B. Lennox and H. Lin",

year = "2010",

language = "English",

pages = "327--332",

booktitle = "Proceedings of the IASTED International Conference on Modelling, Identification and Control|Proc. IASTED Int. Conf. Model. Ident. Control",

note = "29th IASTED International Conference on Modelling, Identification and Control, MIC 2010 ; Conference date: 01-07-2010",

url = "http://www.actapress.com/Abstract.aspx?paperId=37799",

}

Shamekh, A, Lennox, B & Lin, H 2010, Comparison between standard PLS, RLS, and unbiased PLS. in Proceedings of the IASTED International Conference on Modelling, Identification and Control|Proc. IASTED Int. Conf. Model. Ident. Control. pp. 327-332, 29th IASTED International Conference on Modelling, Identification and Control, MIC 2010, Innsbruck, 1/07/10. <http://www.actapress.com/Abstract.aspx?paperId=37799>

TY - GEN

T1 - Comparison between standard PLS, RLS, and unbiased PLS

AU - Shamekh, A.

AU - Lennox, B.

AU - Lin, H.

PY - 2010

Y1 - 2010

N2 - Partial Least Squares algorithm (PLS) is widely used in chemometric studies and increasingly in process engineering applications. For process problems, PLS is typically used as a dynamic modeling technique. Unfortunately, traditional PLS identification techniques will typically produce a model that is biased in its regression parameters. In this article, an effective unbiased recursive PLS algorithm is proposed to address this problem. This paper provides a comparative study, using simulated data, to illustrate the potential benefits of the proposed approach.

AB - Partial Least Squares algorithm (PLS) is widely used in chemometric studies and increasingly in process engineering applications. For process problems, PLS is typically used as a dynamic modeling technique. Unfortunately, traditional PLS identification techniques will typically produce a model that is biased in its regression parameters. In this article, an effective unbiased recursive PLS algorithm is proposed to address this problem. This paper provides a comparative study, using simulated data, to illustrate the potential benefits of the proposed approach.

KW - System identification, ordinary least squares, partial least squares, recursive identification.

M3 - Conference contribution

SP - 327

EP - 332

BT - Proceedings of the IASTED International Conference on Modelling, Identification and Control|Proc. IASTED Int. Conf. Model. Ident. Control

T2 - 29th IASTED International Conference on Modelling, Identification and Control, MIC 2010

Y2 - 1 July 2010

ER -

Comparison between standard PLS, RLS, and unbiased PLS

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Keywords

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