Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

Soft sensor has been extensively utilized in industrial processes for prediction of key quality variables. To build an accurate virtual sensor model, it is very significant to model the dynamic and nonlinear behaviors of process sequential data properly. Recently, a long short-term memory (LSTM) network has shown great modeling ability on various time series, in which basic LSTM units can handle data nonlinearities and dynamics with a dynamic latent variable structure. However, the hidden variables in the basic LSTM unit mainly focus on describing the dynamics of input variables, which lack representation for the quality data. In this paper, a supervised LSTM (SLSTM) network is proposed to learn…

• NN
  National Natural Science Foundation of China

  Awards: 61621062, 61703440, 61590921
• CS
  Central South University

  Award: 2018CX011
• NS
  Natural Science Foundation of Hunan Province

  Award: 2018JJ3687