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Product Quality Prediction Based on BP Neural Network and Rough Set Theory

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International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018 (ATCI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 842))

Abstract

Product quality is critical to the survival and development of enterprises. Quality prediction is an important step in the field of quality control. In this paper, based on the study of BP neural network and rough set theory, the data mining model and algorithm for the combination of BP neural network and rough set theory are designed to give full play to their advantages and overcome the existing problems. Combined with the actual manufacturing process of an enterprise, algorithm solution and simulation analysis are carried out to verify the feasibility of the method and improve the accuracy of product quality prediction.

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Author information

Authors and Affiliations

  1. Mechanical Engineering School, Shenyang University, Shenyang, 110044, Liaoning, China

    Yingying Su & Lianjuan Han

Authors
  1. Yingying Su
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  2. Lianjuan Han
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Corresponding author

Correspondence to Yingying Su .

Editor information

Editors and Affiliations

  1. Faculty of Science, Engineering and Built Environment, Deakin University Geelong, Geelong, VIC, Australia

    Jemal Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  4. Tsinghua University, Beijing Shi, China

    Zheng Xu

  5. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

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Su, Y., Han, L. (2019). Product Quality Prediction Based on BP Neural Network and Rough Set Theory. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_119

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