WSEAS Transactions on Systems
Print ISSN: 1109-2777, E-ISSN: 2224-2678
Volume 12, 2013
New Online RKPCA-RN Kernel Method Applied to Tennessee Eastman Process
Authors: Nadia Souilem, Ilyes Elaissi, Okba Taouali, Hassani Messeouad
Abstract: This paper proposes a new method for online identification of a nonlinear system using RKHS models. The RKHS model is a linear combination of kernel functions applied to the used training set observations. For large datasets, this kernel based to severs computational problems and makes identification techniques unsuitable to the online case. For instance, in the KPCA scheme the Gram matrix order grows with the number of training observations and its eigen decomposition. The proposed method is based on Reduced Kernel Principal Component Analysis technique (RKPCA), to extract the principal component will be time consuming.