NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS

<正> In the industrial process situation, principal component analysis (PCA) is a general method in data reconciliation. However, PCA sometime is unfeasible to nonlinear feature analysis and limited in application to nonlinear industrial process. Kernel PCA (KPCA) is extension of PCA and can be used for nonlinear feature analysis. A nonlinear data reconciliation method based on KPCA is proposed. The basic idea of this method is that firstly original data are mapped to high dimensional feature space by nonlinear function, and PCA is implemented in the feature space. Then nonlinear feature analysis is implemented and data are reconstructed by using the kernel. The data reconciliation method based on KPCA is applied to ternary distillation column. Simulation results show that this method can filter the noise in measurements of nonlinear process and reconciliated data can represent the true information of nonlinear process.