Locally weighted kernel partial least square model for nonlinear processes: A case study
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
Ngu, Joyce Chen Yen ; Yeo, Christine (2022)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 ...
Hirt, Christian (2011)Gravimetric geoid computation is often based on modified Stokes's integration, where Stokes's integral is evaluated with some stochastic or deterministic kernel modification. Accurate numerical evaluation of Stokes's ...
Featherstone, Will; Kirby, Jonathan; Hirt, Christian; Filmer, Michael; Claessens, Sten; Brown, N.; Hu, Guorong; Johnston, G. (2011)AUSGeoid09 is the new Australia-wide gravimetric quasigeoid model that has been a posteriori fitted to the Australian Height Datum (AHD) so as to provide a product that is practically useful for the more direct determination ...