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Virtual sample generation method based on generative adversarial fuzzy neural network

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Abstract

Key performance indicators of complex industrial process such as production quality and pollutant emissions concentration are difficult to be measured online due to limited detection technology and high economical cost. Their modeling samples have high dimension, strong uncertainty, and small sample, which cannot satisfy the needs of traditional machine learning algorithms. A virtual sample generation method based on generative adversarial fuzzy neural network (GAFNN) is proposed to address the abovementioned problems. First, an adaptive feature selection algorithm based on random forest is used to reduce input feature for the original real samples. Second, candidate virtual samples are generated by GAFNN to alleviate the problems of uncertainty and small sample. Third, the virtual samples are screened by a multi-constrained selection mechanism to improve the quality of virtual samples. Finally, a deep forest classification model is constructed on the basis of the mixed samples in terms of the original real and selected virtual samples. The effectiveness of the proposed method is verified on benchmark and real industrial data.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was financially supported by National Natural Science Foundation of China (62073006 and 62021003), Beijing Natural Science Foundation (4212032 and 4192009), National Key Research and Development Program of China (2021ZD0112301 and 2021ZD0112302).

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Canlin Cui, Jian Tang, Heng Xia & Junfei Qiao

  2. Beijing Laboratory of Smart Environmental Protection, Beijing, 100124, China

    Canlin Cui, Jian Tang, Heng Xia & Junfei Qiao

  3. Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education, Beijing, 100124, China

    Canlin Cui, Jian Tang, Heng Xia & Junfei Qiao

  4. Beijing Institute of Artificial Intelligence, Beijing, 100124, China

    Canlin Cui, Jian Tang, Heng Xia & Junfei Qiao

  5. Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), 07360, Mexico City, Mexico

    Wen Yu

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  1. Canlin Cui
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Correspondence to Jian Tang.

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Cui, C., Tang, J., Xia, H. et al. Virtual sample generation method based on generative adversarial fuzzy neural network. Neural Comput & Applic 35, 6979–7001 (2023). https://doi.org/10.1007/s00521-022-08104-5

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