Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure

Probabilistic latent variable regression models have recently caught much attention in the process industry, particularly for soft sensor applications. One of the main challenges for those models is how to effectively extract nonlinear features for latent variable regression. This paper proposes a nonlinear probabilistic latent variable regression (NPLVR) model based on the features extracted by variational auto-encoder. To extend the NPLVR...