Just-in-time learning method based on two kinds of local samples combined with two-stage training parallel learner for nonlinear chemical process soft sensing

Just-in-time Learning (JITL) is a soft sensor method to develop the corresponding local model for each query sample, which has strong adaptability. Aiming at the lack of feature information of dynamic law of chemical processes in similar samples, a JITL method based on two kinds of local samples combined with two-stage training parallel learner (JITLTKLS-TSTPL) was proposed to address this issue. Firstly, similarity measurement and moving window are used to select local samples respectively. And the length of two kinds of samples is optimized by genetic algorithm. Then, the proposed two-stage training parallel learner is proposed to learn local nonlinear features of similar samples and dynamic trend information of dynamic samples by cross-freezing network weights. Finally, the JITL model was verified by three chemical processes. The results show that the total error and maximum error of JITLTKLS-TSTPL are the smallest compared with the JITL method using a single sample for modeling. In the public data set, compared with other research methods, the R2 of JITLTKLS-TSTPL was the highest, reaching 0.983.