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- W2350465340 abstract "Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling.A novel method based on kernel principal components analysis(KPCA) and false nearest neighbor method(FNN) is proposed on select the most suitable secondary process variables used as nonlinear modeling inputs.In the proposed approach,the KPCA can be employed to overcome difficulties encountered with the existing multicollinearity between the factors.In the new KPCA feature subspace,it is inspired by FNN that interpretation of primary variable would be estimated by calculating the variables' map distance in the KPCA space to select secondary variables.Nonlinear model form the production processing of hydrogen cyanide is used to verify the validity of the method,and compared with the fully parametric model.The results show that the method is effective and suitable for variable selection.Therefore,a new method is provided for the variable selection of nonlinear system modeling." @default.
- W2350465340 created "2016-06-24" @default.
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- W2350465340 date "2012-01-01" @default.
- W2350465340 modified "2023-09-23" @default.
- W2350465340 title "Variable Selection for Nonlinear Modeling Based on False Nearest Neighbours in KPCA Subspace" @default.
- W2350465340 hasPublicationYear "2012" @default.
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