基于二子空间协同训练算法的半监督软测量建模

Semi-supervised soft sensor modeling based on two-subspace co-training algorithm

在工业过程中,存在着辅助变量与主导变量数据比例严重失衡的问题。协同训练算法是其中一种利用无标签数据中的潜在信息以提升学习性能的模型训练方法。然而目前在协同训练软测量建模过程中,学习器之间存在严重的训练特性交叉重叠的问题,这将导致对主导变量的预测性能衰减。针对这一问题,提出基于二子空间协同训练算法的半监督软测量模型two-subspace co-training KNN(TSCO-KNN)。该模型将二子空间分块算法与协同训练算法相结合,利用辅助变量与主成分子空间PCS和残差子空间RS两个特征子空间的相关性程度,将数据变量拆分为两个具有显著差异性的学习数据集,进而使用KNN回归器进行协同训练,共同用于对主导变量的预测。最后在乙烯精馏塔塔顶乙烷浓度和TE过程产品浓度软测量中进行仿真研究,验证本文所提算法的有效性。

In the industrial process,there is a serious imbalance between the auxiliary variable and the dominant variable.Co-training algorithm is one of the model training methods that uses potential information in unlabeled data to improve learning performance.However,there is a problem of overlapping training characteristics between learners in the current collaborative training using in soft sensor,which will lead to a decline in the prediction performance of dominant variables.To solve this problem,this paper proposes a TSCO-KNN semi-supervised soft sensor model based on two-subspace co-training algorithm.The model combines the two-subspace algorithm with the existing co-training algorithm.By analyzing the correlation between the auxiliary variables and the PCS and the RS,the variables are split into two different learning data sets,and then the KNN regressor is used for collaborative training,which is jointly used to predict the key quality variable.Finally,a simulation study was carried out in the soft measurement of the ethane concentration at the top of the ethylene distillation tower and the product concentration of the TE process to verify the effectiveness of the algorithm proposed in this paper.