Research Article
A Study of Various Training Algorithms on Neural Network for Angle based Triangular Problem
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 71 - Issue 13 |
| Published: June 2013 |
| Authors: Amarpal Singh, Piyush Saxena, Sangeeta Lalwani |
10.5120/12420-8988
|
Amarpal Singh, Piyush Saxena, Sangeeta Lalwani . A Study of Various Training Algorithms on Neural Network for Angle based Triangular Problem. International Journal of Computer Applications. 71, 13 (June 2013), 30-36. DOI=10.5120/12420-8988
@article{ 10.5120/12420-8988,
author = { Amarpal Singh,Piyush Saxena,Sangeeta Lalwani },
title = { A Study of Various Training Algorithms on Neural Network for Angle based Triangular Problem },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 71 },
number = { 13 },
pages = { 30-36 },
doi = { 10.5120/12420-8988 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A Amarpal Singh
%A Piyush Saxena
%A Sangeeta Lalwani
%T A Study of Various Training Algorithms on Neural Network for Angle based Triangular Problem%T
%J International Journal of Computer Applications
%V 71
%N 13
%P 30-36
%R 10.5120/12420-8988
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper examines the study of various feed forward back-propagation neural network training algorithms and performance of different radial basis function neural network for angle based triangular problem. The training algorithms in feed forward back-propagation neural network comprise of Scale Gradient Conjugate Back-Propagation (BP), Conjugate Gradient BP through Polak-Riebre updates, Conjugate Gradient BP through Fletcher-Reeves updates, One Secant BP and Resilent BP. The final result of each training algorithm for angle based triangular problem will also be discussed and compared.
References
- S. Haykins, "A Comprehensive Foundation on Neural Networks," Prentice Hall, 1999 .
- Neural network specification, available at URL: http://www. mathworks. in/products/neuralnetwork/description3. html
- T. M. Khoshgoftaar, E. B. Allen, J. P. Hudepohl, and S. J. Aud, "Application of neural networks to software quality modeling of a very large telecommunications system", IEEE Transactions on Neural Network, vol. 8, pp. 902-909, 1997.
- D. F, Specht, "A general regression neural network". IEEE Transactions on Neural Networks, vol. 2, Issue: 6, pp. 568-576, 1991.
- Mathworks Online. Available: http://www. www. mathworks. com, 2009.
- Jonathan Richard Shewchuk, an Introduction to the Conjugate Gradient Method without the Agonizing Pain, August 1994.
- Martin Fodslette Moller, A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Neural Networks, 6:525-533, 1993.
- S. N. Sivanadam, S. Sumathi, S. N. Deepa ,"Introduction to Neural Networks Using Matlab 6. 0 " , Tata McGraw Hill , NewDelhi, 2006.
- G. Finnie and G. witting, "AI tools for software Devlopment Effort Estimation", International Conference on Software Engineering: Education and practice, 1996.
- S. N. Sivanandam & S. N. Deepa, Principles of Soft Computing, Wiley Publications
- Zweiri, Y. H. , Whidborne, J. F. and Sceviratne, L. D. A Three-term Backpropagation Algorithm. Neurocomputing. 50: 305-318. 2002.
- 10. C. M. Bishop. Neural Networks for Pattern Recognition. Chapter 7, pp. 253-294. Oxford University Press. 1995.
- Demuth, H. , Beale, M. Neural Network Toolbox (Mathworks, Natick,Massachusetts, 1998). World Academy of Science, Engineering and Technology
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