Nonlinear process monitoring by integrating manifold learning with Gaussian process
Yuan-Jui Liua, Tao Chenb and Yuan Yaoa∗, aDepartment of Chemical Engineering, National Tsing Hua University, Hsinchu, Taiwan, R.O.C, bDepartment of Chemical and Process Engineering, University of Surrey, Guildford GU2 7XH, UK, *To whom correspondence should be addressed: Tel: 886-3-5713690, Fax: 886-3-5715408, Email: firstname.lastname@example.org
In order to monitor nonlinear processes, kernel principal component analysis (KPCA) has become a popular technique. Nevertheless, KPCA suffers from two major disadvantages. First, the underlying manifold structure of data is not considered in process modeling. Second, the selection of kernel function and kernel parameters ...