Show simple item record

dc.contributor.authorJoyce Chen Yen, Ngu
dc.contributor.authorYeo, Christine
dc.identifier.citationJoyce Chen Yen, N. and Yeo, W.S. 2022. Locally weighted kernel partial least square model for nonlinear processes: A case study. ASEAN Journal of Process Control. 1 (1).

A soft sensor, namely locally weighted partial least squares (LW-PLS) cannot cope with the nonlinearity of process data. To address this limitation, Kernel functions are integrated into LW-PLS to form locally weighted Kernel partial least squares (LW-KPLS). In this study, the different Kernel functions including Linear Kernel, Polynomial Kernel, Exponential Kernel, Gaussian Kernel and Multiquadric Kernel were used in the LW-KPLS model. Then, the predictive performance of these Kernel functions in LW-KPLS was accessed by employing a nonlinear case study and the analysis of the obtained results was then compared. In this study, it was found that the predictive performance of using Exponential Kernel in LW-KPLS is better than other Kernel functions. The values of root-mean-square errors (RMSE) for the training and testing dataset by utilizing this Kernel function are the lowest in the case study, which is 44.54% lower RMSE values as compared to other Kernel functions.

dc.titleLocally weighted kernel partial least square model for nonlinear processes: A case study
dc.typeJournal Article
dcterms.source.titleASEAN Journal of Process Control
curtin.departmentGlobal Curtin
curtin.accessStatusOpen access
curtin.facultyGlobal Curtin
curtin.contributor.orcidYeo, Christine [0000-0003-3248-3521]
curtin.contributor.scopusauthoridYeo, Christine [57199053825]

Files in this item


This item appears in the following Collection(s)

Show simple item record
Except where otherwise noted, this item's license is described as