摘要：AbstractSlow feature analysis (SFA) has attracted much attention as a method for dynamic modelling. However, SFA has an inherent limitation in that it assumes that the dynamic behaviour is linear. In this paper, a new criterion for SFA in general dynamic systems is defined based on the motivation of maximising the information retained during system evolution, which is called EVOLVE·INFOMAX. The theoretical properties of this new criterion are rigorously justified, the optimisation function under EVOLVE·INFOMAX is proposed, and a tailored algorithm based on neural networks is designed. The case study on a simulated data set and the Tennessee Eastman process benchmark shows that the proposed method has better performance to extract slow features of nonlinear dynamical systems.
