文献类型：期刊文献

英文题名：Model-Agnostic Meta-Learning With Optimal Alternative Scaling Value and Its Application to Industrial Soft Sensing

作者：Lu, Yusheng[1];Peng, Xin[1];Yang, Dan[1];Yang, Minglei[1];Zhong, Weimin[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2021

卷号：17

期号：12

起止页码：8003

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20210809935937);WOS:【SCI-EXPANDED(收录号:WOS:000690940600014)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program under Grant 61988101), in part by the International (Regional) Cooperation and Exchange Project under Grant 61720106008, and in part by the National Natural Science Foundation of China under Grant 61925305 and Grant 61890930-3.

语种：英文

外文关键词：Training; Adaptation models; Sensors; Predictive models; Informatics; Task analysis; Optimization; Continuous catalytic reforming process; deep learning; meta-learning; online quality prediction; soft sensor

摘要：In soft sensing, relationship variation of process variables and quality indicators may cause the model trained from the training datasets unsuitable for the prediction on the testing datasets. As the model-agnostic meta-learning can utilize the supporting datasets to strengthen the prediction performance of the query samples, it can maintain reliable prediction performance in relationship variation. However, the traditional model-agnostic meta-learning contains inconsistencies between the parameters evaluated in the training stage and those adapted in the predicting stage. The phenomenon is inferred as the dilemma of getting valuable evaluated parameters related to the initial parameters and accurate parameters representing the parameters adapted in the predicting stage. In this article, we propose the stage-related adaption block to use the model-agnostic meta-learning modularly. Finally, the model-agnostic meta-learning method based on the optimal alternative scaling value is proposed and verified in a numerical example and an industrial application.