Quadratic Mutual Information based Regression for Prediction of Quality Variable in Batch Process - Details

Author：

Li, Zheng (Li, Zheng.) | Wang, Pu (Wang, Pu.) | Gao, Xuejin (Gao, Xuejin.) (Scholars：高学金) | Qi, Yongsheng (Qi, Yongsheng.) | Gao, Huihui (Gao, Huihui.)

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CPCI-S EI Scopus

Abstract：

Quality　prediction　is　of　great　importance　for　batch　processes.　Predicting　quality　variable　is　a　challenging　task　because　of　various　factors　such　as　strong　nonlinearity　and　non-Gaussian　exist　in　batch　data.　A　quadratic　mutual　information　based　regression　method　is　proposed　to　handle　the　problem.　The　proposed　method　takes　into　account　higher　order　statistics　that　reveal　the　non-linear　dependencies　between　the　process　variables　and　important　quality　variables.　Furthermore,　the　proposed　method　is　implemented　without　the　hypothesis　of　Gaussian　distribution　of　the　dataset　as　in　MPLS.　The　effectiveness　of　the　QMIR　method　is　illustrated　by　a　dataset　of　industrial　Escherichia　coli　fermentation　process,　compared　with　MPLS.　©　2020　IOP　Publishing　Ltd.　All　rights　reserved.

Keyword：

Forecasting Regression analysis Escherichia coli Higher order statistics Artificial intelligence Batch data processing

Author Community：

[ 1 ] [Li, Zheng]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 2 ] [Li, Zheng]Engineering Research Centre of Digital Community, Ministry of Education, Beijing, China
[ 3 ] [Li, Zheng]Beijing Laboratory for Urban Mass Transit, Beijing, China
[ 4 ] [Li, Zheng]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 5 ] [Wang, Pu]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 6 ] [Wang, Pu]Engineering Research Centre of Digital Community, Ministry of Education, Beijing, China
[ 7 ] [Wang, Pu]Beijing Laboratory for Urban Mass Transit, Beijing, China
[ 8 ] [Wang, Pu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 9 ] [Gao, Xuejin]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 10 ] [Gao, Xuejin]Engineering Research Centre of Digital Community, Ministry of Education, Beijing, China
[ 11 ] [Gao, Xuejin]Beijing Laboratory for Urban Mass Transit, Beijing, China
[ 12 ] [Gao, Xuejin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China
[ 13 ] [Qi, Yongsheng]School of Electric Power, Inner Mongolia University of Technology, Hohhot, China
[ 14 ] [Gao, Huihui]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 15 ] [Gao, Huihui]Engineering Research Centre of Digital Community, Ministry of Education, Beijing, China
[ 16 ] [Gao, Huihui]Beijing Laboratory for Urban Mass Transit, Beijing, China
[ 17 ] [Gao, Huihui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China

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Source ：

ISSN： 1742-6588

Year： 2020

Issue： 1

Volume： 1487

Language： English

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 4

Affiliated Colleges：

信息科学技术学院本学院/部未明确归属的数据

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