article Open AccessTop 10% cited
Performance Analysis of Various Activation Functions in Artificial Neural Networks
Journal of Physics Conference Series · 2019 · Vol. 1237(2) · pp. 022030–022030
Jianli Feng(Xi'an Shiyou University)Shengnan Lu✉(Xi'an Shiyou University)
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
Cited by
A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope
Sustainability · 2023 · 486 citations
References
ImageNet Large Scale Visual Recognition Challenge
International Journal of Computer Vision · 2015 · 39,683 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.
