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- W2388401483 abstract "To the characteristics of strong non linearity,multiple influence factors and large delay of the nonferrous metallurgical process,a hybrid prediction model of the process parameter is proposed by using least square support vector machine(LS-SVM)and kernel partial least squares(KPLS).The original data series of process parameter is decomposed into the sub-series with different frequency characteristics based on wavelet multi-resolution analysis.According to the features of these decomposed components,KPLS model for top layer low frequency time series and different LS-SVM models for high frequency time series are constructed to predict these components.The predicted results of these components are reconstructed to obtain the prediction result.The proposed method is applied to the prediction of effluent cobalt concentration in the cobalt removal purification process in zinc hydrometallurgy.The experimental results shows that the proposed model has better performance than LS-SVM model,and possesses good generalization ability and strong robustness." @default.
- W2388401483 created "2016-06-24" @default.
- W2388401483 creator A5054863124 @default.
- W2388401483 date "2010-01-01" @default.
- W2388401483 modified "2023-09-23" @default.
- W2388401483 title "Prediction Model of Process Parameter Based on KPLS and LS-SVM" @default.
- W2388401483 hasPublicationYear "2010" @default.
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