MOb–GRU neural network for industrial soft sensor modeling method and output prediction

Modeling the integrity of industrial process is a relatively difficult task due to its strong nonlinearity, dynamic characteristics and slow time variability. Though there exist some soft sensing technologies for industrial process, they fail to consider the nonlinear and dynamic characteristics comprehensively of the process. Therefore, this paper proposes a model order based gated recurrent unit (MOb–GRU) neural network soft sensor model for fully-dynamic modeling of nonlinear dynamic process. Specifically, firstly, in terms of the MOb–GRU structure selection, this paper determines the total module number of the network according to the complexity of dynamic characteristics of the actual object. Moreover, the MOb–GRU can flexibly set the number of units for reverse update, which breaks the limitation that the traditional GRU can only output from the first module. Secondly, in order to make the memory network converge to the optimal at a faster rate, this paper designs the network training algorithms based on the adaptive learning rate and the learning rate matrix, respectively. Then, the typical univariate and multivariable nonlinear dynamic processes are selected in the simulation experiment, and the MOb–GRU neural network is used to model and predict them. Finally, the rationality of MOb–GRU network architecture as well as the high efficiency of the training algorithms is demonstrated through the simulation results. © 2022 South China University of Technology. All rights reserved.