文献类型：期刊文献

英文题名：A data enhancement method based on generative adversarial network for small sample-size with soft sensor application

作者：Zhang, Zhongyi[1];Wang, Xueting[2];Wang, Guan[2];Jiang, Qingchao[1];Yan, Xuefeng[1];Zhuang, Yingping[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：186

外文期刊名：COMPUTERS & CHEMICAL ENGINEERING

收录：;EI(收录号：20241916063368);WOS:【SCI-EXPANDED(收录号:WOS:001240714700001)】；

基金：The authors gratefully acknowledge the support from the following foundations: National Key R & D Program of China under Grant No. 2021YFC2101100, National Natural Science Foundation of China under Grants (62322309, 31900073) , Shanghai Science and Technology Commission Program (23S41900500) , and Shanghai Rising-Star Program under Grants (21QA1402400) .

语种：英文

外文关键词：Small sample-size; Generating adversarial network; Soft sensor; Process modeling

摘要：Soft sensor plays an important role in improving product quality; however, practical applications may often face with the problem of small sample size, which is challenging for developing data -driven models in terms of feature selection and good generalization. This paper proposes a data enhancement approach for small sample size datadriven problems based on generative adversarial networks integrated with maximum relevance minimum redundancy (MRMR). First, sample expansion is performed on the initial data by using a generative adversarial network. Second, irrelevant variables are eliminated by the MRMR and optimal features are obtained. Finally, neural networks-based soft sensor modeling is performed using the augmented dataset and the selected features. The proposed method is tested on a simulated penicillin case, an actual penicillin production case and an actual erythromycin production case. Experimental results show that the proposed method outperforms state -of -the -art existing methods, which verify the effectiveness and superiority of the proposed method.