Soft sensor design using transductive moving window learner

Abstract

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 (MWtr). 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 MWtr is higher than both MW, and JITL models, and MWtr 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 MWtr a convenient adaptive method in industrial soft sensor design.