Research on semi-supervised heterogeneous adaptive co-training soft-sensor model

Soft-sensing technology is widely applied to the prediction of important and difficult to measure variables online in industrial processes. However, due to the complexity of industrial processes, non-linearity and high costs to acquire data, the ratio of input and output variables data required for modeling is seriously unbalanced. Therefore, depending on the existing co-training model, this paper combines the co-training algorithm with the back propagation neural network (BP) algorithm to propose a co-training BP model for nonlinear problems. However, due to the time variability and uncertainty of the application process, as well as the negative influences of external environment, and so on, the data exhibit mutation, delay and high volatility, even the prediction performance of the model deteriorated. Thus, this paper proposed a semi-supervised heterogeneous adaptive co-training RPLS-RBP model. On the one hand, the model used odd-even grouping to equalize two parts of the labeled data. On the other hand, RPLS and RBP are used simultaneously for modeling and the prediction on labeled data. To demonstrate the prediction performance of the model, the proposed model is verified by a simulation benchmark platform (Benchmark Simulation Model-1) and a real sewage treatment plant (UCI database). The results show that the proposed model achieved better prediction performance. © All Right Reserved.