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• W2471656365 abstract "This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA) for handling nonlinear dynamic systems. The proposed method is entitled Moving Window Reduced Kernel Principal Component Analysis (MW-RKPCA). It consists firstly in approximating the principal components (PCs) of the KPCA model by a reduced data set that approaches properly the system behavior in the order to elaborate an RKPCA model. Secondly, the proposed MW-RKPCA consists on updating the RKPCA model using a moving window. The relevance of the proposed MW-RKPCA technique is illustrated on a Tennessee Eastman process." @default.
• W2471656365 created "2016-07-22" @default.
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• W2471656365 date "2016-09-01" @default.
• W2471656365 modified "2023-09-26" @default.
• W2471656365 title "Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring" @default.
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• W2471656365 doi "https://doi.org/10.1016/j.isatra.2016.06.002" @default.
• W2471656365 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27342996" @default.
• W2471656365 hasPublicationYear "2016" @default.
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