Abstract:

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.

Key words: soft-sensor, semi-supervised, co-training, recursive PLS, recursive BP

摘要：

软测量技术被广泛应用到工业过程中重要且难以在线测量变量的预测。然而，由于工业过程的复杂性,非线性和高昂的数据获取成本，使得建模所需的输入和输出变量数据比例严重不平衡。因此，本文在已有的co-training模型的基础上，将协同训练算法与前馈神经网络（BP）算法相结合，提出了针对非线性问题的co-training BP模型。然而，由于软测量模型应用过程的时变性和不确定性，以及外部环境等因素的影响，会造成数据突变、延迟和波动性大等情况，导致模型预测性能的衰减。因此，本文提出了一种半监督异构的自适应co-training RPLS-RBP模型。一方面，该模型使用奇偶分组的方法将标记数据进行两部分均分。另一方面，递归PLS（RPLS）与递归BP（RBP）同时用于标记数据的建模和预测。为了验证模型的预测性能，所提出模型在一个污水处理的仿真基准平台(BSM1)和一个实际污水厂(UCI)的数据中得到了验证。结果表明，所提模型具有较好的预测性能。

关键词: 软测量, 半监督, 协同训练, 递归PLS, 递归BP