Locally weighted kernel partial least square model for nonlinear processes: A case study
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 functi...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
2022
|
| Online Access: | http://mypcs.com.my/journal/index.php/ajpc/article/view/6/2 http://hdl.handle.net/20.500.11937/89384 |
| _version_ | 1848765210357137408 |
|---|---|
| author | Joyce Chen Yen, Ngu Yeo, Christine |
| author_facet | Joyce Chen Yen, Ngu Yeo, Christine |
| author_sort | Joyce Chen Yen, Ngu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:31:38Z |
| format | Journal Article |
| id | curtin-20.500.11937-89384 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:31:38Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-893842022-10-17T04:10:01Z Locally weighted kernel partial least square model for nonlinear processes: A case study Joyce Chen Yen, Ngu Yeo, Christine 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/89384 http://mypcs.com.my/journal/index.php/ajpc/article/view/6/2 http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Joyce Chen Yen, Ngu Yeo, Christine Locally weighted kernel partial least square model for nonlinear processes: A case study |
| title | Locally weighted kernel partial least square model for nonlinear processes: A case study |
| title_full | Locally weighted kernel partial least square model for nonlinear processes: A case study |
| title_fullStr | Locally weighted kernel partial least square model for nonlinear processes: A case study |
| title_full_unstemmed | Locally weighted kernel partial least square model for nonlinear processes: A case study |
| title_short | Locally weighted kernel partial least square model for nonlinear processes: A case study |
| title_sort | locally weighted kernel partial least square model for nonlinear processes: a case study |
| url | http://mypcs.com.my/journal/index.php/ajpc/article/view/6/2 http://hdl.handle.net/20.500.11937/89384 |