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DOI: 10.14569/IJACSA.2019.0100406
PDF

Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks

Author 1: Marina Adriana Mercioni
Author 2: Alexandru Tiron
Author 3: Stefan Holban

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 4, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The paper proposes the dynamic modification of the activation function in a learning technique, more exactly backpropagation algorithm. The modification consists in changing slope of sigmoid function for activation function according to increase or decrease the error in an epoch of learning. The study was done using the Waikato Environment for Knowledge Analysis (WEKA) platform to complete adding this feature in Multilayer Perceptron class. This study aims the dynamic modification of activation function has changed to relative gradient error, also neural networks with hidden layers have not used for it.

Keywords: Artificial neural networks; activation function; sigmoid function; WEKA; multilayer perceptron; instance; classifier; gradient; rate metric; performance; dynamic modification

Marina Adriana Mercioni, Alexandru Tiron and Stefan Holban, “Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 10(4), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100406

@article{Mercioni2019,
title = {Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100406},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100406},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {4},
author = {Marina Adriana Mercioni and Alexandru Tiron and Stefan Holban}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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