Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 4
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.
Authors and Affiliations
Marina Adriana Mercioni, Alexandru Tiron, Stefan Holban
Keywords
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- EP ID EP550253
- DOI 10.14569/IJACSA.2019.0100406
- Views 114
- Downloads 0
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