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- W2980489158 abstract "Just-in-time (JIT) adaptive soft sensors have been widely used in chemical processes because they can deal with slow-varying processes, abrupt process changes, and outliers. However, these traditional JIT algorithms including locally weighted partial least square (LW-PLS) have limitations in dealing with non-Gaussian distributed and nonlinear data. To address these issues, a modified LW-PLS-based JIT algorithm, namely, ensemble locally weighted independent component kernel partial least square (E-LW-IC-KPLS) is proposed. Its predictive performances were tested using the data generated from a numerical example and two simulated plants. Then, the results were compared to the ones resulted from LW-PLS, locally weighted kernel partial least square (LW-KPLS), and locally weighted independent component kernel partial least square (LW-IC-KPLS) algorithms. From these comparative studies, it is evident that E-LW-IC-KPLS is superior compared to its traditional counterparts concerning predictive performances. The pr..." @default.
- W2980489158 created "2019-10-25" @default.
- W2980489158 creator A5046339640 @default.
- W2980489158 creator A5084147876 @default.
- W2980489158 date "2019-10-17" @default.
- W2980489158 modified "2023-10-14" @default.
- W2980489158 title "Adaptive Soft Sensor Development for Non-Gaussian and Nonlinear Processes" @default.
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- W2980489158 doi "https://doi.org/10.1021/acs.iecr.9b03821" @default.
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