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Performance Analysis of Various Activation Functions in Artificial Neural Networks

Journal of physics. Conference series, 2019-06, Vol.1237 (2), p.22030 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1237/2/022030

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  • Title:
    Performance Analysis of Various Activation Functions in Artificial Neural Networks
  • Author: Feng, Jianli ; Lu, Shengnan
  • Subjects: Artificial neural networks ; Neural networks
  • Is Part Of: Journal of physics. Conference series, 2019-06, Vol.1237 (2), p.22030
  • Description: The development of Artificial Neural Networks (ANNs) has achieved a lot of fruitful results so far, and we know that activation function is one of the principal factors which will affect the performance of the networks. In this work, the role of many different types of activation functions, as well as their respective advantages and disadvantages and applicable fields are discussed, so people can choose the appropriate activation functions to get the superior performance of ANNs.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1237/2/022030
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    IOP 英国物理学会OA刊
    ProQuest Central
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