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Predicting the Free Calcium Oxide Content on the Basis of KPCA and Support Vector Machines

SHU Yun-xing1,2,XU Chao2,LI Gang-yan1 (1.School of Mechatronic Engineering,Wuhan University of Technology,Wuhan 430070,China;2.Luoyang Institute of Science and Technology,Luoyang 471003,China)  
Kernel principal component analysis(KPCA) and support vector machines(SVM) were combined in this study,which employed KPCA to conduct nonlinear feature extraction from the data sample and obtained feature principal components that are easier for regression operations.The number of input space dimensions that could lower the SVM has been met.After that,training was conducted by using the least squares support vector machines(LS-SVM) and determined the optimal parameters of the LS-SVM by means of grid searching and cross validation.A KPCA-SVM-based model was then established to predict the free calcium content in the clinker.Finally,our calculation results proved that the model proposed in this study can effectively predict the free calcium content in the clinker.
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