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
Reseach Article

Improved Multi-Layer Perceptron for Recognition of Control Chart Pattern

by O. El Farissi, H. Elboujaoui
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 36
Year of Publication: 2020
Authors: O. El Farissi, H. Elboujaoui
10.5120/ijca2020920537

O. El Farissi, H. Elboujaoui . Improved Multi-Layer Perceptron for Recognition of Control Chart Pattern. International Journal of Computer Applications. 176, 36 ( Jul 2020), 39-42. DOI=10.5120/ijca2020920537

@article{ 10.5120/ijca2020920537,
author = { O. El Farissi, H. Elboujaoui },
title = { Improved Multi-Layer Perceptron for Recognition of Control Chart Pattern },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 36 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number36/31438-2020920537/ },
doi = { 10.5120/ijca2020920537 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:23.220657+05:30
%A O. El Farissi
%A H. Elboujaoui
%T Improved Multi-Layer Perceptron for Recognition of Control Chart Pattern
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 36
%P 39-42
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This work presents a prediction of control chart pattern using a Neural Network Multilayer. A Multilayer model configuration of one hidden layer with nonlinear sigmoid activation and the Bayesian algorithm, is used. Good results with hay accuracy obtained shows that the neural network is performant to predict the control chart pattern.

References
  1. O. EL FARISSI and all, Recognition Improvement of Control Chart Pattern Using Artificial Neural Networks, International Review on Modelling and Simulations (I.RE.M.O.S.), Vol. 8, N. 2 ISSN 1974-9821 April 2015.
  2. 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.
  3. W. Laosiritaworn and T.t Bunjongjit, Classification Techniques for Control Chart Pattern Recognition: A Case of Metal Frame for Actuator Production, Chiang Mai J. Sci, 40(4): 701-712, 2013.
  4. A. D. karaoglan, An Integrated Neural Network Structure for Recognizing Autocorrelated and Trending Processes, Mathematical and Computational Applications, Vol. 16, No. 2, pp. 514-523, 2011.
  5. M. A. Hadiyat and K. R. Prilianti, Comparing Statistical Feature and Artificial Neural Networks for Control 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.
  6. A. S. Anagun, A Neural Network Applied to Pattern Recognition in Statistical Process Control, Computers & Industrial Engineering 35, 185-188, 1998.
  7. 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.
  8. 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.
  9. Fionn Murtagh, Multilayer perceptrons for classification and regression, Neurocomputing 1990.
  10. R. Vendrame, R. S. Braga, Y. Takahata ; and D. S. Galvão: Structure-Activity Relationship Studies of Carcinogenic Activity of Polycyclic Aromatic Hydrocarbons Using Calculated Molecular Descriptors with Principal Component Analysis and Neural Network Methods, J. Chem. Inf. Comput. Sci. 1999.
  11. E. Agirre-Basurko, G. Ibarra-Berastegi, I. Madariaga, Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environmental Modelling & Software, 21, 4, 430-446, 2006.
  12. 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).
Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Multi-Layer Perceptron (MLP) Control Charts Control Charts Pattern (CCP)