IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v40y2009i1p69-77.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077907005346
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2007.07.033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yaguang Kong & Chenfeng Xie & Song Zheng & Peng Jiang & Meng Guan & Fang Wang, 2019. "Dynamic Early Warning Method for Major Hazard Installation Systems in Chemical Industrial Park," Complexity, Hindawi, vol. 2019, pages 1-18, May.
    2. Jielin Li & Cyril Chol Chol Deng & Jiye Xu & Zhongjian Ma & Pei Shuai & Liangbing Zhang, 2023. "Safety Risk Assessment and Management of Panzhihua Open Pit (OP)-Underground (UG) Iron Mine Based on AHP-FCE, Sichuan Province, China," Sustainability, MDPI, vol. 15(5), pages 1-23, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. Bekiros, Stelios & Kouloumpou, Dimitra, 2020. "SBDiEM: A new mathematical model of infectious disease dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    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. 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.
    7. 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.
    8. Pang, Guoping & Chen, Lansun, 2007. "A delayed SIRS epidemic model with pulse vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 34(5), pages 1629-1635.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:40:y:2009:i:1:p:69-77. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.