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Activation functions selection for BP neural network model of ground surface roughness

Author

Listed:
  • Yuhang Pan

    (Dalian University of Technology)

  • Yonghao Wang

    (Dalian University of Technology)

  • Ping Zhou

    (Dalian University of Technology)

  • Ying Yan

    (Dalian University of Technology)

  • Dongming Guo

    (Dalian University of Technology)

Abstract

Roughness prediction of ground surfaces is critical in understanding and optimizing the grinding process. However, it is hitherto difficult to predict accurately the ground surface roughness by theoretical and empirical models due to the complexity of grinding process. BP neural network (BPNN), which can be used to establish the relationship between processing parameters and surface roughness, avoids the difficulty of revealing the complex physical mechanism and thus has unique potential in automatic optimization of grinding process in industrial practice. Activation function is one of the most important factors affecting the efficiency and accuracy of BPNN. Nevertheless, it is often selected arbitrarily or at most by trials or tuning. This paper proposes an activation function selection approach in which virtual data generated from the approximate physical model are employed to evaluate the performance of the BPNN in practice application. The results show that with tansig as the activation function of hidden layer and purelin as the activation function of output layer, the BPNN model can obtain the highest learning efficiency. Moreover, when the activation function of hidden layer is sigmoid, whose shape factor is 1–3, and the output layer activation function is purelin, the model can predict more precisely. Finally, the proposed approach is validated by comparing the performance of BPNN obtained from the virtual data and the experimental data. Obtained results showed that the proposed approach is a simple and effective way to determine the activation function of BPNN.

Suggested Citation

  • Yuhang Pan & Yonghao Wang & Ping Zhou & Ying Yan & Dongming Guo, 2020. "Activation functions selection for BP neural network model of ground surface roughness," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1825-1836, December.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01538-5
    DOI: 10.1007/s10845-020-01538-5
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    References listed on IDEAS

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    1. Bai, Yanping & Zhang, Haixia & Hao, Yilong, 2009. "The performance of the backpropagation algorithm with varying slope of the activation function," Chaos, Solitons & Fractals, Elsevier, vol. 40(1), pages 69-77.
    2. Zhongwei Liang & Shaopeng Liao & Yiheng Wen & Xiaochu Liu, 2019. "Working parameter optimization of strengthen waterjet grinding with the orthogonal-experiment-design-based ANFIS," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 833-854, February.
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    Cited by:

    1. Zengya Zhao & Sibao Wang & Zehua Wang & Shilong Wang & Chi Ma & Bo Yang, 2022. "Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 943-952, April.
    2. Xubao Liu & Yuhang Pan & Ying Yan & Yonghao Wang & Ping Zhou, 2022. "Adaptive BP Network Prediction Method for Ground Surface Roughness with High-Dimensional Parameters," Mathematics, MDPI, vol. 10(15), pages 1-18, August.

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