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- W2324972893 abstract "Soft sensors have been widely used for process control in industrial plants to estimate difficult-to-measure process variables online. A genetic algorithm-based process variables and dynamics selection (GAVDS) method is one method used to select important process variables and optimal time-delays of each variable simultaneously. However, the GAVDS method cannot handle a nonlinear relationship between X and an objective variable y because linear regression is used as a modeling technique. We therefore proposed a region selection method based on GAVDSand support vector regression (SVR), which is a nonlinear regression method. The proposed method is named GAVDS-SVR. We applied GAVDS-SVR to simulation data having high correlation between close pairs of X-variables and a nonlinear relationship between X and y. The GAVDS-SVR method could select regions of X-variables appropriately by considering the nonlinearity and could construct predictive models with high accuracy. Through soft-sensor analysis of industrial polymer process data, we confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed method." @default.
- W2324972893 created "2016-06-24" @default.
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- W2324972893 date "2013-01-01" @default.
- W2324972893 modified "2023-10-16" @default.
- W2324972893 title "Development of Nonlinear Soft Sensor Methods Considering Process Dynamics" @default.
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- W2324972893 doi "https://doi.org/10.9746/sicetr.49.206" @default.
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