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: | , |
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| Format: | Journal Article |
| Published: |
2022
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| Online Access: | http://mypcs.com.my/journal/index.php/ajpc/article/view/6/2 http://hdl.handle.net/20.500.11937/89384 |
| Summary: | 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. |
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