Abstract
This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA) for handling nonlinear dynamic systems. The proposed method is entitled Moving Window Reduced Kernel Principal Component Analysis (MW-RKPCA). It consists firstly in approximating the principal components (PCs) of the KPCA model by a reduced data set that approaches “properly” the system behavior in the order to elaborate an RKPCA model. Secondly, the proposed MW-RKPCA consists on updating the RKPCA model using a moving window. The relevance of the proposed MW-RKPCA technique is illustrated on a Tennessee Eastman process.
•This paper proposes a new Reduced Kernel Principal Component Analysis (RKPCA) technique entitled Moving Window RKPCA (MW-RKPCA).•The proposed MW-RKPCA provides lower computation time and memory complexity.•It consists on using RKPCA technique to select a reduced set of observations that ‘sufficiently’ approaches the system behavior.•The updating procedure of the RKPCA model is achieved using a moving window technique.•The proposed MW-RKPCA algorithm has been evaluated on Tennessee Eastman process.