SVM Modeling and Application Based on Kernel Function Principal Component Analysis

Kernel function is introduced into PCA method to obtain nonlinear character information from experimental data so as to overcome the disadvantage of traditional methods in nonlinear modeling.SVM is utilized in developing soft sensor that uses the nonlinear characteristics of data as the input of SVM.Application shows that the proposed method is effective and superior to both PCA-SVM and KPCA-NN methods in the application of acrylonitrile transforming prediction.