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Performance Analysis of Various Activation Functions in Artificial Neural Networks

Journal of Physics Conference Series · 2019 · Vol. 1237(2) · pp. 022030–022030
Jianli FengShengnan Lu

Abstract

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.

Neural Networks and ApplicationsIndustrial Vision Systems and Defect DetectionAdvanced Neural Network ApplicationsActivation functionArtificial neural networkComputer scienceArtificial intelligencePrincipal (computer security)Function (biology)Affect (linguistics)Machine learningPsychologyBiology

Funding

  • Canadian Institute for Advanced Research
Citations
190
FWCI
11.87
field-weighted impact
References
7
Percentile
99%
vs. same field & year
Citations per year
References
ImageNet Large Scale Visual Recognition Challenge
International Journal of Computer Vision · 2015 · 39,683 citations
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Performance Analysis of Various Activation Functions in Artificial Neural Networks · Scinovex