文献类型：期刊文献

英文题名：Quality-relevant feature extraction method based on teacher-student uncertainty autoencoder and its application to soft sensors

作者：Lu, Yusheng[1];Jiang, Chao[1];Yang, Dan[1];Peng, Xin[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：2022

卷号：592

起止页码：320

外文期刊名：INFORMATION SCIENCES

收录：;EI(收录号：20220711620531);WOS:【SCI-EXPANDED(收录号:WOS:000796947000021)】；

基金：The authors declare no competing financial interest. This work was supported in part by the National Natural Science Foundation of China under Grant 61890930-3, 61925305, and 61803157, the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Fundamental Research Funds for the Central Universities under Grant 222202217006.

语种：英文

外文关键词：Autoencoder; Representation learning; Soft sensor; Teacher-student network

摘要：Supervised representation learning based on the teacher-student framework can extract quality-related features for soft sensors, in which the teacher network extracts representation information for the student network as supervision information. In traditional applications, the teacher network is heavy and is difficult to train, so the teacher network is conventionally pre-trained. However, the pre-training of the teacher network is unnecessary if the training process is not complicated so that it is meaningful to joint optimize the teacher-student network. In our application, the teacher-student framework is used to extract quality-related representation information for soft sensors. The objective is to maximize the mutual information of representation information and supervision information, in which the inconsistency of distributions between observed information and supervisory information is modeled as isotropic Gaussian noise. The objective is decoupled through analysis under some approximate assumptions so that the alternative iteration method can be used to update the parameters of the model. The proposed quality-related feature extraction method is applied to soft sensors combined with a traditional just-in-time learning method. Our experiments show that the prediction performance of our representation extraction method is better than other existing representation extraction algorithms. (C) 2022 Published by Elsevier Inc.