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Prediction of SARS epidemic by BP neural networks with online prediction strategy

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  • Bai, Yanping
  • Jin, Zhen

Abstract

The method of predicting BP neural networks is used for SARS epidemic to improve the existing computational methods, and better accuracy of prediction is achieved. A suitable momentum term is added to BP algorithm to accelerate the convergence speed. An online prediction strategy is applied to monitor the training and predicting process. We have achieved a series of predicting results of SARS epidemic about Beijing and Shanxi in China.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:26:y:2005:i:2:p:559-569
    DOI: 10.1016/j.chaos.2005.01.064
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    Cited by:

    1. Pang, Guoping & Chen, Lansun, 2007. "A delayed SIRS epidemic model with pulse vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 34(5), pages 1629-1635.
    2. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    3. Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    4. Zhang, Tailei & Teng, Zhidong, 2008. "Global asymptotic stability of a delayed SEIRS epidemic model with saturation incidence," Chaos, Solitons & Fractals, Elsevier, vol. 37(5), pages 1456-1468.
    5. Yan Hao & Ting Xu & Hongping Hu & Peng Wang & Yanping Bai, 2020. "Prediction and analysis of Corona Virus Disease 2019," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    6. Awawdeh, Fadi & Adawi, A. & Mustafa, Z., 2009. "Solutions of the SIR models of epidemics using HAM," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 3047-3052.
    7. 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.
    8. Guerra, Fábio A. & Coelho, Leandro dos S., 2008. "Multi-step ahead nonlinear identification of Lorenz’s chaotic system using radial basis neural network with learning by clustering and particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 35(5), pages 967-979.
    9. Ya Li & Zhanguo Wei, 2022. "Regional Logistics Demand Prediction: A Long Short-Term Memory Network Method," Sustainability, MDPI, vol. 14(20), pages 1-17, October.

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