Comparison between standard PLS, RLS, and unbiased PLS

A. Shamekh, B. Lennox, H. Lin

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
    Title of host publicationProceedings of the IASTED International Conference on Modelling, Identification and Control|Proc. IASTED Int. Conf. Model. Ident. Control
    Pages327-332
    Number of pages5
    Publication statusPublished - 2010
    Event29th IASTED International Conference on Modelling, Identification and Control, MIC 2010 - Innsbruck
    Duration: 1 Jul 2010 → …
    http://www.actapress.com/Abstract.aspx?paperId=37799

    Conference

    Conference29th IASTED International Conference on Modelling, Identification and Control, MIC 2010
    CityInnsbruck
    Period1/07/10 → …
    Internet address

    Keywords

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

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