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- W3174572783 abstract "Data-driven dynamic predictive models have been extensively applied in dynamic process systems for online predicting difficult-to-measure key variables. However, due to the limitations of industrial data in quantity, quality and representativity, the generalization performance of the developed dynamic models are not reliable. In other words, based on certain unique historical dataset, high-accuracy of the predictive model for online unseen data cannot be guaranteed. To deal with this issue, this paper proposes a switching model-based dynamic modeling framework for improving the generalization accuracy and reliability of data-driven predictive models, which is composed of two stages, i.e., the offline model library construction and online model switch. The performance of the proposed scheme is assessed by a real-life industrial dynamic process using switching dynamic partial least squares models, and its application potentials are verified through comparisons among the state-of-the-art methods." @default.
- W3174572783 created "2021-07-05" @default.
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- W3174572783 date "2021-05-14" @default.
- W3174572783 modified "2023-10-16" @default.
- W3174572783 title "Improving the Generalization Performance of Data-driven Predictive Model for Dynamic Process Systems" @default.
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- W3174572783 doi "https://doi.org/10.1109/ddcls52934.2021.9455521" @default.
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