Performances of Various Back-propagation Learning Algorithms of Neural Network Using Matlab

Authors

  • Md. Ashek- Al-Aziz Associate Professor, University of Development Alternative (UODA), Dhaka-1209, Bangladesh
  • Abdullah -Hil Muntakim Assistant Professor and Assistant Director, University of Development Alternative (UODA), Dhaka-1209, Bangladesh

Keywords:

Neural Network, Back-propagation, Training, Testing

Abstract

There are plenty of back-propagation learning algorithms of artificial neural network. Performances of various back-propagation learning algorithms have been checked using few portions of Australian Rain Dataset. Polak-Ribiere conjugate gradient back-propagation and Levenberg-Marquardt back-propagation have showed good performance than others.

References

. Kapil Nahar, Artificial Neural Network, Compusoft, International Journal of Advanced Computer Technology, Vol. 1, No. 1, 2012

. Stuart J. Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, Third Edition, Prentice Hall, ISBN 9780136042594, 2010

. Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016). Back-Propagation and Other Differentiation Algorithms, Deep Learning. MIT Press. pp. 200–220. ISBN 9780262035613.

. Md. Ashek-Al-Aziz, Abdullah-Hil Muntakim, Md. Kawshik Ahmed, No Regular Behavior Pattern in Neural Network Execution – A Matlab Experience, International Journal of Computer Applications, Vol. 174, No. 19, February 2021

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Published

2021-04-24

How to Cite

Al-Aziz, M. A.-., & -Hil Muntakim, A. (2021). Performances of Various Back-propagation Learning Algorithms of Neural Network Using Matlab. International Journal of Sciences: Basic and Applied Research (IJSBAR), 57(2), 112–136. Retrieved from https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/12524

Issue

Vol. 57 No. 2 (2021)

Section

Articles

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