We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Using Artificial Neural Networks for Recognition of Control Chart Pattern

by El Farissi.o, Moudden.a, Benkachcha.s
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 3
Year of Publication: 2015
Authors: El Farissi.o, Moudden.a, Benkachcha.s
10.5120/20319-2388

El Farissi.o, Moudden.a, Benkachcha.s . Using Artificial Neural Networks for Recognition of Control Chart Pattern. International Journal of Computer Applications. 116, 3 ( April 2015), 46-50. DOI=10.5120/20319-2388

@article{ 10.5120/20319-2388,
author = { El Farissi.o, Moudden.a, Benkachcha.s },
title = { Using Artificial Neural Networks for Recognition of Control Chart Pattern },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 3 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 46-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number3/20319-2388/ },
doi = { 10.5120/20319-2388 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:56:05.901828+05:30
%A El Farissi.o
%A Moudden.a
%A Benkachcha.s
%T Using Artificial Neural Networks for Recognition of Control Chart Pattern
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 3
%P 46-50
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Control charting is an important tool in SPC to improve the quality of products. Unnatural patterns in control charts assume that an assignable cause affecting the process is present and some actions must be applied to overcome the problem. By its automatic and fast recognition ability the neural network provide best performance to immediately recognize process trends. In this paper, a neural network model is used to control chart pattern recognition (CCPR). Several forms of architectures have been tested and the results point out a network configuration which leads to excellent quality of recognition.

References
  1. N. V. N. Indra Kiran, Effective Control Chart Pattern Recognition Using Artificial Neural Networks, IJCSNS International Journal of Computer Science and Network Security, VOL. 10 No. 3, March 2010 .
  2. W. Laosiritaworn and T. t Bunjongjit,Classification Techniques for Control Chart Pattern Recognition: A Case of Metal Frame for ActuatorProduction,Chiang Mai J. Sci, 40(4) : 701-712, 2013.
  3. A. D. karaoglan,An Integrated Neural Network Structure for RecognizingAutocorrelated and Trending Processes, Mathematical and Computational Applications, Vol. 16, No. 2, pp. 514-523, 2011.
  4. M. A. Hadiyat and K. R. Prilianti, Comparing Statistical Feature and Artificial Neural Networks forControl Chart Pattern Recognition: A Case Study,The 3rd International Conference on Technology and Operations Management"Sustaining Competitiveness through Green Technology Management"Bandung – Indonesia, July 4-6, 2012.
  5. A. S. Anagun, A Neural Network Applied to Pattern Recognition in Statistical Process Control, Computers & Industrial Engineering 35, 185-188, 1998.
  6. R. S. Guh and J. D. T. Tannock, Recognition of Control Chart Concurrent Patterns Using a Neural Network Approach, International Journal of Production Research 37, 1743-1765, 1999.
  7. N. V. N. IndraKiran,M. Pramiladevi and G. Vijaya Lakshmi, Training Multilayered Perceptrons for Pattern Recognition: A Comparative Study of Five Training Algorithms, IMECS, Vol. I, March 2011.
  8. B. M. Wilamowski, Neural Network Architectures. Industrial Electronics Handbook (vol. 5 – Intelligent Systems, 2nd Edition, chapter 6, pp. 6-1 to 6-17, CRC Press, 2011).
  9. A. E . Smith, X-bar and R control chart interpretation using neural computing, International Journal of Production Research, 32(2), 309-320, 1994.
  10. G. Zhang, B. E. Patuwo, and M. Y. Hu, Forecasting With Artificial Neural Networks , The state of the art, International Journal of Forecasting. Vol. 14, , p 35–62, 1998.
  11. S. Makridakis, and M. Hibon, Evaluating Accuracy (or Error) Measures, Working Paper, INSEAD, Fontainebleau, France, 1995
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks (ANN) Statistical Process Control (SPC) Control Charts (CC) Control Charts Pattern (CCP).

Powered by PhDFocusTM