Probabilistic PCA Based Spatio-Temporal Multi-Modeling for Distributed Parameter Processes

【摘要】：正Data-based modeling of unknown distributed parameter systems(DPSs) is very challenging due to their infinite-dimensional,nonlinear and even time-varying dynamics.To get a low-order model for applications,the principal component analysis(PCA) is often used.However,as a linear dimension reduction,it only leads to one set of fixed spatial bases.Therefore a good performance for nonlinear and time-varying DPSs could not be guaranteed.In this study,a probabilistic PCA based spatio-temporal multi-modeling is proposed.Due to its multi-modeling mechanism,a better performance can be achieved,which is demonstrated by simulations.