doi: 10.7763/IJFCC.2013.V2.228
The Impact of Training Iterations on ANN Applications Using BPNN Algorithm
Qamar Abbas
,
Waqas Haider Bangyal
,
Jamil Ahmad
- Iraq University, Islamabad, Pakistan.
Abstract
Training Artificial Neural Network (ANN) hasattracted many researchers for a long time. This paperinvestigates the impact of training iterations of ANN usingBackpropagation Neural Network (BPNN) algorithm. The twosets of adjustable parameters, i.e., the learning rate andnumber of hidden nodes in the hidden layer are used to analyzethe impact of training iterations of ANN applications is used.The applications that are used in this research are XORproblem and Digit Recognition. The efficacy of the resultsusing BPNN algorithm is shown through an analysis of theimpact of training iterations and by presenting simulationresults from two different applications.
Keywords
- XOR
- digit
- training iterations
- BPNN
How to Cite
Qamar Abbas, Waqas Haider Bangyal, and Jamil Ahmad, "The Impact of Training Iterations on ANN Applications Using BPNN Algorithm," International Journal of Future Computer and Communication, vol. 2, no. 6, pp. 567-569, 2013. https://doi.org/ 10.7763/IJFCC.2013.V2.228
Copyright & License
Copyright © 2013 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).