CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process

by Olatunde A. Adeoti, Peter A. Osanaiye
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 20
Year of Publication: 2013
Authors: Olatunde A. Adeoti, Peter A. Osanaiye
10.5120/12085-8031

Olatunde A. Adeoti, Peter A. Osanaiye . Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process. International Journal of Computer Applications. 69, 20 ( May 2013), 8-12. DOI=10.5120/12085-8031

@article{ 10.5120/12085-8031,
author = { Olatunde A. Adeoti, Peter A. Osanaiye },
title = { Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 20 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number20/12085-8031/ },
doi = { 10.5120/12085-8031 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:45.938072+05:30
%A Olatunde A. Adeoti
%A Peter A. Osanaiye
%T Effect of Training Algorithms on the Performance of ANN for Pattern Recognition of Bivariate Process
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 20
%P 8-12
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Neural Network (ANN) which is designed to mimic the human brain have been used in the literature for identifying variable(s) that is(are) responsible for out-of-control signal and the training algorithms have played a significant role in the identification of the aberrant variable(s). In this paper the effect of three algorithms in the training of ANN for pattern recognition of bivariate process is studied. Situations in which the algorithms performed satisfactorily with respect to recognition accuracy (in percentages), epochs and MSE were identified. The result of study shows that the Levenberg-Marquardt (trainlm) is the best algorithm for pattern recognition of bivariate manufacturing process in terms of recognition accuracy and the resilient backpropagation (trainrp) is best in terms of speed and mean square error performance.

References
  1. Adeoti O. A. and Osanaiye P. A. 2012. Performance Analysis of ANN on Dataset Allocations for Pattern Recognition of Bivariate Process. Mathematical Theory and Modelling, 2(10), pp 53- 63
  2. Alt F. B. 1984. Multivariate quality control. The Encyclopedia of Statistical Sciences Kotz S, Johnson NL, Read CR (eds). Wiley: New York, pp 110-122,
  3. Aparisi F, Avendano G, Sanz J. 2006. Techniques to interpret T2 control chart signals. IIE Transactions 38(8), pp 647–657
  4. Bersimis S, Psarakis S and Panaretos J. 2007. Multivariate Statistical Process Control Charts: An overview. Quality and Reliability Engineering International 23, pp 517-543
  5. Cheng C. S. 1997. A neural network approach for the analysis of control chart patterns. International Journal of Productions Research 35, pp 667-697
  6. Chen L. H and Wang T. Y. 2004. Artificial Neural Networks to classify mean shifts from multivariate X2 chart signals. Computers and Industrial Engineering 47, pp. 195-205.
  7. Crosier R. B. 1988. Multivariate generalizations of cumulative sum quality-control schemes. Technometrics 30, pp 291-303
  8. Demuth H, Beale M and Hagan A. 2010. Neural Network Toolbox User's Guide Math Works, Natick, 2010
  9. Dennis, J. E. and Schnabel R. B. 1983. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, NJ
  10. Guh, R. S. 2007. Online Identification and Quantification of Mean Shifts in Bivariate Process using a Neural Network-based Approach. Quality and Reliability Engineering International 23, pp 367-385.
  11. Hagan A, Demuth H and Beale M. 1996. Neural Network Design. PWS Publishing
  12. Hagan M. T and Menhaj M. B. 1994. Training Feeddforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks 5(6), pp 989-993
  13. Healy J. D. 1987. A note on multivariate CUSUM procedures. Technometrics 29, pp 409- 412
  14. Hotelling, H. 1947. Multivariate Quality Control-Illustrated by the Air Testing of Sample Bombsights. Techniques of Statistical Analysis (Eisenhart, C. , Hastay, M. W. , and Wallis, W. A. eds. ), McGraw Hill, New York,
  15. Jackson J. 1985. "Multivariate quality control. Communications in Statistics-Theory and Methods 14, pp 2657-2688
  16. Lowry C. A, Woodall W. H, Champ C. W and Rigdon S. E. 1992. A multivariate EWMA control chart. Technometrics 34, pp 46-53
  17. Mason, Robert L. , Tracy, Nola D. , and Young, John C. 1995. Decomposition of T2 for Multivariate Control Chart Interpretation. Journal of Quality Technology 27(2), pp 99-109.
  18. Mason R. L, Champ C. W, Tracy N. D, Wierda R. J and Young J. C. 1997. Assessment of Multivariate process control techniques. Journal of Quality Technology 29, pp 140-143
  19. Murphy, B. J. 1987. Selecting out of control variables with the T2 multivariate quality control procedure. The Statistician 36, 1987, pp 571–583
  20. Niaki S. T. A, Abbasi B. 2005. Fault diagnosis in multivariate control chart using artificial neural networks. Quality Reliability Engineering International 21:825–840
  21. Pignatiello J. J and Runger G. C. 1990. Comparisons of multivariate CUSUM charts. Journal of Quality Technology 22, pp 173-186
  22. Prabhu S. S and Runger G. C. 1997. Designing a multivariate EWMA control chart. Journal of Quality Technology 29, pp 8-15
  23. Riedmiller M and Braun H. 1993. A direct Adaptive Method for faster backpropagation learning: The RPROP algorithm. Proceedings of IEEE international conference on neural networks, San Francisco, CA
  24. Woodall W. H and Ncube M. M. 1985. Multivariate CUSUM quality control procedures. Technometrics 27, pp 285-292
Index Terms

Computer Science
Information Sciences

Keywords

Artificial neural network training algorithms feedforward multilayer perceptron recognition accuracy

Powered by PhDFocusTM