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Reseach Article

Article:A Novel Approach for Pattern Recognition

by Prashanta Ku. Patra, Swati Vipsita, Subasish Mohapatra, Sanjit Ku. Dash
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 8
Year of Publication: 2010
Authors: Prashanta Ku. Patra, Swati Vipsita, Subasish Mohapatra, Sanjit Ku. Dash
10.5120/1406-1899

Prashanta Ku. Patra, Swati Vipsita, Subasish Mohapatra, Sanjit Ku. Dash . Article:A Novel Approach for Pattern Recognition. International Journal of Computer Applications. 9, 8 ( November 2010), 19-23. DOI=10.5120/1406-1899

@article{ 10.5120/1406-1899,
author = { Prashanta Ku. Patra, Swati Vipsita, Subasish Mohapatra, Sanjit Ku. Dash },
title = { Article:A Novel Approach for Pattern Recognition },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 9 },
number = { 8 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume9/number8/1406-1899/ },
doi = { 10.5120/1406-1899 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:03.492659+05:30
%A Prashanta Ku. Patra
%A Swati Vipsita
%A Subasish Mohapatra
%A Sanjit Ku. Dash
%T Article:A Novel Approach for Pattern Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 9
%N 8
%P 19-23
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, Optical Back Propagation and Levenberg Marquardt (LM) algorithms are used for Pattern Recognition. These two algorithms are compared with Classical Back Propagation algorithm with varying Learning rate and Momentum .The simulation results are obtained and are shown in different graphs. The corresponding simulation results show the efficiency of Levenberg Marquardt (LM) algorithms in comparison to Optical Back Propagation Algorithm and Back Propagation Algorithm.

References
  1. Bishop,C.M. 1995. Neural Networks for Pattern Recognition, Oxford.
  2. Mohammed A. Otair, Walid A. Salameh , Speeding Up Back-Propagation Neural Networks, Proceedings of the 2005 Informing Science and IT Education Joint Conference, pp. 167-173.
  3. Fredric M.Ham, Ivica kostanic, Principles of Neurocomputing for Science and Engineering (TATA McGraw-Hill 2002 chapter 1 -pg 3, 19, 26.
  4. Ralston,A. , Reilly,E.D, 1993 . Encyclopedia of Computer Science, Van Nostrand Reinhold. 3rd Ed.
  5. Liu,Y. and H.Ma, Ding, 1991, Pattern recognition using ω-orbit finite automata, K. H. Tzao, Editor, Proc. SPIE 1606, Boston, MA, Nov. 1991, pp.226-240.
  6. Satish Kumar, Neural Networks –A Classroom Approach TATA McGraw-Hill- ISBN 0-07-048292-6, pg-61,166,188.
  7. Simon Haykin , 2009. Neural Networks, A comprehensive Foundation, 2nd ed., Pearson Prentice Hall.
  8. Michalopoulos,D.,Hu,C,K.,2002, An error backpropagation artificial neural networks application in automatic car license plate recognition, Lecture Notes in Computer Science, 2002, volume 2358/2002.
  9. Werbos, P. J. 1988. Backpropagation : Past and future, Proceeding of International Conference on Neural Networks, San Diego ,CA, 343-354.
  10. Mishra,D., Yadav,A. Ray,S., and Kalra,P,K. , Levenberg-Marquardt Learning Algorithm for Integrate-and-Fire NeuronModel , Neural Information Processing - Letters and Reviews Vol.9, No.2, November 2005.
  11. Lera , G. and Pinzolas. M., Neighborhood Based Levenberg–Marquardt Algorithm for Neural Network Training, IEEE Transactions on Neural Networks, Vol. 13, No. 5, September 2002.
  12. Rajasekaran,S., Vijayalakshmi Pai G,A. Neural Network, Fuzzy Logic and Genetic Algorithm , Prentice Hall of India, pg-13-20.
  13. Pawlicki,T. , Lee,D,S.,Hull,J. and Srihari,S. ,Neural Networks and their application to handwritten digit recognition, Proc. IEEE International Conference on Neural Networks: Vol. II, San Diego,CA, 1988, pp.63-70.
  14. Rumelhart, D. E., Hinton,G. E. and Williams, J,, Learning representations by back- propagating errors”, Nature,vol.323,pp.533-536,1986.
  15. Otair, M. A. & Salameh, W. A. (2004), An improved back-propagation neural networks using a modified non-linear function, Proceedings of the IASTED International Conference, 2004, pp. 442-447.
Index Terms

Computer Science
Information Sciences

Keywords

Back propagation Optical back propagation Learning rate Momentum