Regression on dynamic PLS structures for supervised learning of dynamic data

Yining DONG, S. Joe QIN*

*Corresponding author for this work

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

88 Citations (Scopus)

Abstract

Partial least squares (PLS) regression is widely used to capture the latent relationship between inputs and outputs in static system modeling. Several dynamic PLS algorithms have been proposed to capture the characteristics of dynamic data. However, none of these algorithms provides an explicit expression for the dynamic inner and outer models. In this paper, a dynamic inner PLS algorithm is proposed for dynamic data modeling. The proposed algorithm provides an explicit dynamic inner model that is ensured in deriving the outer model. Several examples are presented to demonstrate the effectiveness of the proposed algorithm.
Original languageEnglish
Pages (from-to)64-72
Number of pages9
JournalJournal of Process Control
Volume68
Early online date11 May 2018
DOIs
Publication statusPublished - Aug 2018
Externally publishedYes

Bibliographical note

This work is supported by funds from the National Natural Science Foundation of China (61490704) and the Fundamental Disciplinary Research Program of the Shenzhen Committee on Science and Innovations (20160207, 20170155).

Keywords

  • Data-driven modeling
  • Dynamic partial least square

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