Dynamic-Inner Partial Least Squares for Dynamic Data Modeling

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

45 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)117-122
Journal / PublicationIFAC-PapersOnLine
Volume48
Issue number8
Online published25 Sep 2015
Publication statusPublished - 2015
Externally publishedYes

Conference

Title9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015
PlaceCanada
CityWhistler
Period7 - 10 June 2015

Abstract

Partial least squares(PLS) regression has been widely used to capture the relationship between inputs and outputs in static system modeling. Several dynamic PLS algorithms were proposed to capture the characteristic of dynamic systems. However, none of these algorithms provides an explicit description for dynamic inner model and outer model. In this paper, a dynamic inner PLS is proposed for dynamic system modelling. The proposed algorithm gives explicit dynamic inner model and makes inner model and outer model consistent at the same time. Several examples are given to show the effectiveness of the proposed algorithm.

Research Area(s)

  • Data-driven modeling, Dynamic partial least squares