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The performance of the backpropagation algorithm with varying slope of the activation function

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  • Bai, Yanping
  • Zhang, Haixia
  • Hao, Yilong

Abstract

Some adaptations are proposed to the basic BP algorithm in order to provide an efficient method to non-linear data learning and prediction. In this paper, an adopted BP algorithm with varying slope of activation function and different learning rates is put forward. The results of experiment indicated that this algorithm can get very good performance of training. We also test the prediction performance of our adopted BP algorithm on 16 instances. We compared the test results to the ones of the BP algorithm with gradient descent momentum and an adaptive learning rate. The results indicate this adopted BP algorithm gives best performance (100%) for test example, which conclude this adopted BP algorithm produces a smoothed reconstruction that learns better to new prediction function values than the BP algorithm improved with momentum.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:40:y:2009:i:1:p:69-77
    DOI: 10.1016/j.chaos.2007.07.033
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    References listed on IDEAS

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    1. Bai, Yanping & Jin, Zhen, 2005. "Prediction of SARS epidemic by BP neural networks with online prediction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 26(2), pages 559-569.
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    Cited by:

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    3. 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.

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