关键词: 核主元分析 , 关联向量机 , 软测量 , 对羧基苯甲醛

Abstract: A novel modeling method integrated KPCA with RVM was proposed． The kernel primary component analysis （KPCA） was employed to identify the principal components from the nonlinear transform data of independent variables，which were regarded as character variables．Regression between character variables and dependent variables was done based on RVM，and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model．Thus ，KPCA－RVM method could eliminate the disturbance of redundant information and achieve the best nonlinear model with good generalization performance．The method of KPCA－RVM was demonstrated by a 4－CBA’s content soft －sensing of PTA．Simulation results show this method is effective and the performance is better than those of PCA－RVM and RVM．

Key words: Kernel PCA , Relevance vector machine , Soft sensor , ４－carbexybenzaldehyde