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A data enhancement method based on generative adversarial network for small sample-size with soft sensor application.
- Source :
-
Computers & Chemical Engineering . Jul2024, Vol. 186, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- • A data enhancement method based on generative adversarial network is proposed. • The method combines the advantages of data augmentation and feature selection. • Case studies on a simulated case and two real industrial processes are provided. • Comparisons to state-of-the-arts show effectiveness of the proposed method. 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 data-driven 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00981354
- Volume :
- 186
- Database :
- Academic Search Index
- Journal :
- Computers & Chemical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 177248122
- Full Text :
- https://doi.org/10.1016/j.compchemeng.2024.108707