Dynamic-Inner Partial Least Squares for Dynamic Data Modeling
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
---|---|
Pages (from-to) | 117-122 |
Journal / Publication | IFAC-PapersOnLine |
Volume | 48 |
Issue number | 8 |
Online published | 25 Sept 2015 |
Publication status | Published - 2015 |
Externally published | Yes |
Conference
Title | 9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015 |
---|---|
Place | Canada |
City | Whistler |
Period | 7 - 10 June 2015 |
Link(s)
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
Citation Format(s)
Dynamic-Inner Partial Least Squares for Dynamic Data Modeling. / Dong, Yining; Qin, S. Joe.
In: IFAC-PapersOnLine, Vol. 48, No. 8, 2015, p. 117-122.
In: IFAC-PapersOnLine, Vol. 48, No. 8, 2015, p. 117-122.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review