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Reseach Article

Pattern Recognition of Process Mean Shift using Combined ANN Recognizer

by Olatunde A. Adeoti, Rotimi F. Afolabi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 10
Year of Publication: 2012
Authors: Olatunde A. Adeoti, Rotimi F. Afolabi
10.5120/8789-2774

Olatunde A. Adeoti, Rotimi F. Afolabi . Pattern Recognition of Process Mean Shift using Combined ANN Recognizer. International Journal of Computer Applications. 55, 10 ( October 2012), 15-19. DOI=10.5120/8789-2774

@article{ 10.5120/8789-2774,
author = { Olatunde A. Adeoti, Rotimi F. Afolabi },
title = { Pattern Recognition of Process Mean Shift using Combined ANN Recognizer },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 10 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number10/8789-2774/ },
doi = { 10.5120/8789-2774 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:52.432242+05:30
%A Olatunde A. Adeoti
%A Rotimi F. Afolabi
%T Pattern Recognition of Process Mean Shift using Combined ANN Recognizer
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 10
%P 15-19
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Neural Network (ANN) based model has been proposed for diagnosis of process mean shift. These are mainly generalized-based where only a single classifier was applied in the diagnosis of abnormal pattern. In this paper, we analyze the performance of a combined recognizer consisting of small-sized artificial neural networks on varying number of nodes in the hidden layer trained with Levenberg Marquardt and Quasi-Newton Algorithm. The results of our study illustrate the effectiveness of the combined recognizer and showed that combined recognizer performed better when number of hidden nodes is small, say, less than 15 in terms of recognition accuracies and mean square error as compared to the single recognizer.

References
  1. Alt, F. B. 1985 "Multivariate Quality Control," Encyclopedia of the Statistical Science (Kotz, S. , Johnson, N. L. , and Read, C. R. eds. ) 6, 110-122
  2. Anagun, A. S. 1998. A neural network applied to pattern Recognition in Statistical Process Contro. Computers and Industrial Engineering 35, 185-188
  3. Chen L. H and Wang T. Y 2004. Artificial Neural Networks to classify mean shifts from multivariate chart signals. Computers and Industrial Engineering 47, 195-205.
  4. Crosier R. B. 1988. Multivariate Generalizations of cumulative sum quality control schemes. Technometrics 30, 291–303.
  5. Doganaksoy N, Faltin F. W and Tucker W. T 1991, Identification of Out of Control Quality Characteristics in a Multivariate Manufacturing Environment," Communication in Statistics-Theory and Method, 20(9), 2775-2790.
  6. 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, 367-385.
  7. Healy J. D 1987 A note on multivariate CUSUM procedures Technometrics 29,409- 412
  8. 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.
  9. Jackson, J. E. 1985 Multivariate quality control. Communications in Statistics– Theory and Methods 14, 2657–2688.
  10. Lowry, C. A. , Woodall, W. H. , Champ, C. W. and Rigdon, S. E. 1992. A multivariate exponentially weighted moving average control chart. Technometrics 34, 46–53
  11. Maqsood, I. , Khan, M. R. , and Abraham, A. 2004. An ensemble of neural networks for weather forecasting. Neural Computing and Application 13, 112-122.
  12. Maravelakis P. E, Bersimis S, Panaretos J and Psarakis S. 2002. Identifying the out of control variable in a multivariate control chart. Communication in Statistics-Theory and Method 31(12), 2391–2408
  13. Mason, R. L. , Tracy, N. D. and Young, J. C. 1995. Decomposition of T2 for Multivariate Control Chart Interpretation. Journal of Quality Technology, 27, 99– 108.
  14. Mason, R. L. , Tracy, N. D. and Young, J. C. 1997. A practical approach for interpreting multivariate T2 control chart signals. Journal of Quality Technology, 29, 396–406
  15. Murphy, B. J. 1987. Selecting out of control variables with the T2 multivariate quality control procedure. The Statistician, 36: 571–583.
  16. Niaki S. T. A and Abbasi B. 2005. Fault diagnosis in multivariate control chart using artificial neural networks. Quality Reliability Engineering International 21,825–840 .
  17. Pham, D. T and Oztemel, E 1992. Control Chart pattern Recognition using neural networks. Journal of system Engineering. 2, 256-262
  18. Pham, D. T and Wani, M. A. 1999. Feature-based control chart pattern recognition. International Journal of Production Research 35(7),1875-1890
  19. Pignatiello, J. J. Jr and Runger, G. C. 1990. Comparisons of multivariate CUSUM charts. Journal of Quality Technology, 22, 173–186
  20. Sepulveda A and Nachlas J. A. 1997. A simulation approach to multivariate control. Computers and Industrial Engineering 33,113–116
  21. Wani ,M. A and Pham, D. T. 1999. Efficient control chart pattern recognition through synergestic and distributed artificial neural network. Proc. Instn Mech Engrs 213 Part B, 157-169
  22. Woodall, W. H. and Ncube, M. M. 1985. Multivariate CUSUM quality control procedures. Technometrics, 27, 285–292.
Index Terms

Computer Science
Information Sciences

Keywords

Bivariate Statistical Process Control Combined ANN Recognizer Pattern Recognition Recognition Accuracy Mean Square Error