TY - JOUR
T1 - Regression on dynamic PLS structures for supervised learning of dynamic data
AU - Dong, Yining
AU - Qin, S. Joe
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 - Dynamic partial least square
KW - Data-driven modeling
UR - http://www.scopus.com/inward/record.url?scp=85046831682&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85046831682&origin=recordpage
U2 - 10.1016/j.jprocont.2018.04.006
DO - 10.1016/j.jprocont.2018.04.006
M3 - RGC 21 - Publication in refereed journal
SN - 0959-1524
VL - 68
SP - 64
EP - 72
JO - Journal of Process Control
JF - Journal of Process Control
ER -