Dynamic-Inner Partial Least Squares for Dynamic Data Modeling

Yining DONG, S. Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

60 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)117-122
Number of pages6
Issue number8
Early online date25 Sept 2015
Publication statusPublished - 2015
Externally publishedYes
Event9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015 - , Canada
Duration: 7 Jun 201510 Jun 2015


  • Data-driven modeling
  • Dynamic partial least squares


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