Changes in characteristics of the industrial processes require implementation of adaptive mechanisms in soft sensors. In the current study, we propose combining two common adaptation methods, moving window (MW) and Just-In-Time-Learning (JITL), using transductive inference (MW_tr). Transductive learning exploits the knowledge from the feature values of the query points in determining predictions of the response variable at these points. Here, we use JITL predictions for obtaining predictions for the query points, which are used in training the MW learner in a weighted Lasso regression setting. Tests of the proposed method on three publicly available industrial real datasets show that prediction accuracy of MW_tr is higher than both MW, and JITL models, and MW_tr is able to combat against various types of concept drifts via integrating the capabilities of MW and JITL methods. Ease of implementation, robustness and stability render MW_tr a convenient adaptive method in industrial soft sensor design.

工业过程特性的变化要求在软传感器中实现自适应机制。在当前的研究中，我们建议使用转导推理（MW _tr）结合两种常见的适应方法，即移动窗口（MW）和即时学习（JITL ）。转换学习利用查询点的特征值中的知识来确定在这些点处的响应变量的预测。在这里，我们使用JITL预测来获取查询点的预测，这些预测点用于在加权Lasso回归设置中训练MW学习器。对三种公开的工业真实数据集进行的方法测试表明，MW _tr的预测精度均高于MW模型，JITL模型和MW _tr模型。通过集成MW和JITL方法的功能，可以应对各种类型的概念漂移。易于实施，鲁棒性和稳定性使MW _tr在工业软传感器设计中成为一种方便的自适应方法。