Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Afzal, Saduf
- Comparative Study of Adaptive Learning Rate with Momentum and Resilient Back Propagation Algorithms for Neural Net Classifier Optimization
Abstract Views :357 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
Source
International Journal of Distributed and Cloud Computing, Vol 2, No 1 (2014), Pagination: 1-6Abstract
Learning algorithms are generally used to optimize the convergence of neural networks. We need to optimize the convergence of neural networks in order to increase the speed and accuracy of decision making process. One such algorithm that is used to facilitate the optimization process is back propagation learning algorithm. The objective of this study is to compare the performance of two variations of back propagation learning algorithm (Adaptive learning rate with momentum and Resilient). Both the algorithms are experimented on a variety of classification problems in order to assess the efficiency of these two learning approaches. Experimental results reveal that during testing and training Resilient propagation algorithm outperforms back propagation with Adaptive learning rate and momentum.Keywords
ANN, Back-Propagation, RPROP, Learning Rate, Momentum.
References
- Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annual Eugenics, 7(2), 179-188.
- Howard, D. & Mark, B. (2002). Neural Network Toolbox for use with Matlab. User's Guide Version 4.
- Wahed, M. A. (2008). Adaptive learning rate versus Resilient back propagation for numeral recognition. Journal of Al-Anbar University for Pure Science, 2(1), 94-105.
- Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning Internal Representations by Error Propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge: MIT Press.
- Braun, R. H. (1993). A Direct Adaptive Method for Faster Back-Propagation Learning: The RPROP Algorithm. Paper presented at the Proceedings of the International Conference on Neural Networks (pp. 586-591).
- Riedmiller, M. (1994). RPROP Description and Implementation Details Technical Report. University of Karlsruhe W-76128 Karlsruhe.
- Saduf, M. A. W. (2013). Comparative study of back-propagation algorithms for neural networks. International Journal of Advanced Research in Computer Science & Software Engineering, 3(12), 1151-1156.
- Wolberg, W. H. & Mangasarian, O. L. (1990). Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology. Paper presented at National Academy of Sciences, 87, 919-996.
- Zhang, G. P. (2000). Neural Networks for Classification: A Survey. IEEE Transactions on Systems Man and Cybernetics. Part C: Applications and Reviews, 30(4), 451-462.