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

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 stu...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 10; no. 4
Main Authors: Mercioni, Marina Adriana, Tiron, Alexandru, Holban, Stefan
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 2019
Subjects:
Algorithms
Artificial neural networks
Back propagation
Back propagation networks
Machine learning
Multilayer perceptrons
Neural networks
ISSN:2158-107X, 2156-5570
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2019.0100406