A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes
Soft sensors are inferential estimators when the employment of hardware sensors is inapplicable, expensive, or difficult in industrial plant processes. Currently, a simple soft sensor, namely locally weighted partial least squares (LW-PLS), which can cope with the nonlinearity of the process, has...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
Comporter SRL
2022
|
| Online Access: | http://hdl.handle.net/20.500.11937/88287 |
| _version_ | 1848765000166932480 |
|---|---|
| author | Ngu, Joyce Chen Yen Yeo, Christine |
| author_facet | Ngu, Joyce Chen Yen Yeo, Christine |
| author_sort | Ngu, Joyce Chen Yen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Soft sensors are inferential estimators when the employment of hardware sensors is
inapplicable, expensive, or difficult in industrial plant processes. Currently, a simple soft sensor, namely
locally weighted partial least squares (LW-PLS), which can cope with the nonlinearity of the process,
has been developed. However, LW-PLS exhibits the disadvantages of handling strong nonlinear process
data. To address this problem, Kernel functions are integrated into LW-PLS to form locally weighted
Kernel partial least squares (LW-KPLS). Notice that a minimal study was carried out on the impact of
different kernel functions that have not been integrated with the LW-KPLS, in which this model has the
potential to be applied to different chemical-related nonlinear processes. Thus, this study investigates
the predictive performance of LW-KPLS with several different Kernel functions using three nonlinear
case studies. As the results, the predictive performances of LW-KPLS with Polynomial Kernel are better
than other Kernel functions. The values of root-mean-square errors (RMSE) and error of approximation
(Ea) for the training and testing dataset by utilizing this Kernel function are the lowest in their respective
case studies, which are 34.60% to 95.39% lower for RMSEs values and 68.20% to 95.49% smaller for
Ea values. |
| first_indexed | 2025-11-14T11:28:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-88287 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:28:17Z |
| publishDate | 2022 |
| publisher | Comporter SRL |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-882872022-05-06T08:05:18Z A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes Ngu, Joyce Chen Yen Yeo, Christine Soft sensors are inferential estimators when the employment of hardware sensors is inapplicable, expensive, or difficult in industrial plant processes. Currently, a simple soft sensor, namely locally weighted partial least squares (LW-PLS), which can cope with the nonlinearity of the process, has been developed. However, LW-PLS exhibits the disadvantages of handling strong nonlinear process data. To address this problem, Kernel functions are integrated into LW-PLS to form locally weighted Kernel partial least squares (LW-KPLS). Notice that a minimal study was carried out on the impact of different kernel functions that have not been integrated with the LW-KPLS, in which this model has the potential to be applied to different chemical-related nonlinear processes. Thus, this study investigates the predictive performance of LW-KPLS with several different Kernel functions using three nonlinear case studies. As the results, the predictive performances of LW-KPLS with Polynomial Kernel are better than other Kernel functions. The values of root-mean-square errors (RMSE) and error of approximation (Ea) for the training and testing dataset by utilizing this Kernel function are the lowest in their respective case studies, which are 34.60% to 95.39% lower for RMSEs values and 68.20% to 95.49% smaller for Ea values. 2022 Journal Article http://hdl.handle.net/20.500.11937/88287 10.33263/BRIAC132.184 http://creativecommons.org/licenses/by/4.0/ Comporter SRL fulltext |
| spellingShingle | Ngu, Joyce Chen Yen Yeo, Christine A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes |
| title | A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes |
| title_full | A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes |
| title_fullStr | A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes |
| title_full_unstemmed | A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes |
| title_short | A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes |
| title_sort | comparative study of different kernel functions applied to lw-kpls model for nonlinear processes |
| url | http://hdl.handle.net/20.500.11937/88287 |