Supervised Nonlinear Dynamic System for Soft Sensor Application aided by Variational Auto-encoder

Dynamic data modeling has caught much attention from researchers and been introduced into the probabilistic latent variable model in the process industry. It is an enormous challenge to extend these dynamic probabilistic latent variable models to nonlinear forms. In this paper, a supervised nonlinear dynamic system based on variational auto-encoder is introduced for processes with dynamic behaviors and nonlinear characteristics. Based on the framework of variational auto-encoder, which has a probabilistic data representation and a high fitting ability, the supervised nonlinear dynamic system can extract effective nonlinear features for latent variable regression. The feasibility of the proposed supervised nonlinear dynamic system are tested on two Numerical examples and an industrial case. Detailed comparisons verify the effectiveness and superiority of the proposed model.