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A data enhancement method based on generative adversarial network for small sample-size with soft sensor application
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DOI：10.1016/j.compchemeng.2024.108707.png)
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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.
Keyword:
Small sample-size
Generating adversarial network
Soft sensor
Process modeling
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3.9
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8.1K
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1.7W
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