An Ensemble Deep Belief Network Model Based on Random Subspace for NO(x) Concentration Prediction

[Image: see text] An effective NO(x) prediction model is the basis for reducing pollutant emissions. In this paper, a real-time NO(x) prediction model based on an ensemble deep belief network (DBN) is proposed. Variable importance projection analysis is adopted to screen variables, the time delay of each variable is estimated, and the phase space of the original sample is reconstructed by analyzing the historical data. An ensemble strategy based on random subspace is presented, including the data set partition method and ensemble mode of the model. First, subspaces are constructed according to the component information extracted by partial least squares. Then, the deep belief network is used as a submodel. Finally, a back propagation neural network is developed for model combination. The ensemble deep belief network model has been used to model the NO(x) emission prediction of a 660 MW boiler. The simulation results show that the ensemble DBN model can fully exploit the nonlinear mapping relationship between input variables and NO(x) concentration by using various learning learners. Compared with the back propagation neural network and support vector machine, which are commonly used in NO(x) modeling, the ensemble DBN model has better prediction performance and generalization ability.