Model reduction for spatio-temporal systems based on KPCA and LS-SVRM

A novel model reduction technology based on Kernel Principal Component Analysis (KPCA) and Least squares support vector regression machine (LS-SVRM) for spatio-temporal systems. The process of the method is divided into two stages: Firstly, Kernel PCA is used for feature extraction and the necessary instructions for kernel parameter selected is given; Subsequently the temporal dynamics is constructed by LS-SVRM approach; In final, the accuracy and efficiency of the proposed method are verified and contrast to KL-LS-SVRM through the case of a tubular reactor.