Multi‐similarity measurement driven ensemble just‐in‐time learning for soft sensing of industrial processes

Just‐in‐time learning (JITL) technique has been widely used for adaptive soft sensing of nonlinear processes. It builds online local model with the most relevant samples from historical dataset whenever a query sample comes. Hence, the prediction performance greatly depends on the similarity measurement for relevant sample selection. Different similarity measurements have been developed for sample selection in the past decades. However, each method only focuses on one aspect of sample similarity and has its own limitations. Moreover, it is difficult to obtain the similarity mechanism of real process data. A single similarity measurement does not always provide satisfactory prediction performance. To deal with this problem, a novel ensemble just‐in‐time learning (E‐JITL) framework is proposed in this paper. In E‐JITL, different similarity measurements are adopted for sample selection. Then, local prediction models are constructed and trained to estimate the output of the query data with different groups of relevant samples corresponding to the similarity measurements. At last, a final prediction can be obtained by an ensemble strategy on each local model. The effectiveness of the E‐JITL is validated on two industrial applications.