Regression on dynamic PLS structures for supervised learning of dynamic data

Yining DONG; S. Joe QIN

doi:10.1016/j.jprocont.2018.04.006

Regression on dynamic PLS structures for supervised learning of dynamic data

Yining DONG, S. Joe QIN^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

102 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 language	English
Pages (from-to)	64-72
Number of pages	9
Journal	Journal of Process Control
Volume	68
Early online date	11 May 2018
DOIs	https://doi.org/10.1016/j.jprocont.2018.04.006
Publication status	Published - Aug 2018
Externally published	Yes

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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10.1016/j.jprocont.2018.04.006

Cite this

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title = "Regression on dynamic PLS structures for supervised learning of dynamic data",

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.",

keywords = "Data-driven modeling, Dynamic partial least square",

author = "Yining DONG and QIN, {S. Joe}",

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).",

year = "2018",

month = aug,

doi = "10.1016/j.jprocont.2018.04.006",

language = "English",

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journal = "Journal of Process Control",

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T1 - Regression on dynamic PLS structures for supervised learning of dynamic data

AU - DONG, Yining

AU - QIN, S. Joe

N1 - 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).

PY - 2018/8

Y1 - 2018/8

N2 - 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.

AB - 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.

KW - Data-driven modeling

KW - Dynamic partial least square

UR - http://www.scopus.com/inward/record.url?scp=85046831682&partnerID=8YFLogxK

U2 - 10.1016/j.jprocont.2018.04.006

DO - 10.1016/j.jprocont.2018.04.006

M3 - Journal Article (refereed)

SN - 0959-1524

VL - 68

SP - 64

EP - 72

JO - Journal of Process Control

JF - Journal of Process Control

ER -

Regression on dynamic PLS structures for supervised learning of dynamic data

Abstract

Bibliographical note

Keywords

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