On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design

  • J. A. Vazquez-Lopez Instituto Tecnologico de Celaya CIATEC, A.C. Posgrado Interinstitucional en Ciencia y Tecnologia (PICyT) Industrial Engineering Department Celaya, Gto. MEXICO E-mail:
  • I. Lopez-Juarez Centro de Investigacion y de Estudios Avanzados del IPN - Unidad Saltillo Robotics and Advanced Manufacturing Department Carretera Saltillo-Monterrey Km 13. Ramos Arizpe, Coahuila. MEXICO
  • M. Peña-Cabrera National Autonomous University of Mexico Engineering Computing Science and Automation Department Institute of Research on Applied Mathematics and Systems Mexico, City. MEXICO

Abstract

Time-series statistical pattern recognition is of prime importance in statistics, especially in quality control techniques for manufacturing processes. A frequent problem in this application is the complexity when trying to determine the behaviour (pattern) from sample data. There have been identified standard patterns which are commonly present when using the X chart; its detection depends on human judgement supported by norms and graphical criteria. In the last few years, it has been demonstrated that Artificial Neural Networks (ANN’s) are useful to predict the type of time-series pattern instead of the use of rules. However, the ANN control parameters have to be fixed to values that maximize its performance. This research proposes an experimental design methodology to determine the most appropriate values for the control parameters of the FuzzyARTMAP ANN such as: learning rate (β ) and network vigilance (ρa, ρb, ρab) in order to increment the neural network efficiency during unnatural pattern recognition.

References

[1] D.C. Montgomery. Introduction to Statistical Quality Control. Third edition. John Wiley & Sons, New York, 1991.

[2] R.S. Guh. Real-time pattern recognition in statistical process control: a hybrid neural network/decision tree-based approach. IMechE, Part B: J. Engineering Manufacture. Vol. 219, No. 3. pp. 283-298, 2005.
http://dx.doi.org/10.1243/095440505X28963

[3] T. L. Lucy-Bouler. Application to forecasting of neural network recognition of shifts and trends in quality control data. Proceedings of the World Congress on Neural Networks (WCNN'93). Vol. 1, pp. 631-633, UK, 1993.

[4] R.S. Guh. Robustness of the neural network based control chart pattern recognition system to nonnormality. Int. J. Qual. Reliability Mgmt. Vol. 19(1), pp. 97-112, 2002.
http://dx.doi.org/10.1108/02656710210415749

[5] C.W. Zobel, D.F. Cook, Q.J. Nottingham. An augmented neural network classification approach to detecting mean shifts in correlated manufacturing process parameters. Int. Journal of Production Research. Vol. 42 (4), pp. 741-758, 2004.
http://dx.doi.org/10.1080/00207540310001602856

[6] F. Zorriassantine, J.D.T. Tannock. A review of neural networks for statistical process control. Journal of Intelligent Manufacturing. Vol. 9, pp. 209-224, 1998.
http://dx.doi.org/10.1023/A:1008818817588

[7] C.S. Cheng. A multi-layer neural network model for detecting changes in the process mean. Computers Ind. Engng. Vol. 28(1), pp. 51-61, 1995.
http://dx.doi.org/10.1016/0360-8352(94)00024-H

[8] H.B. Hwarng, and C. W. Chong. Detecting process nonrandomness through a fast and cumulative learning ART-based pattern recognizer. Int. J. Prod. Res., Vol. 33(7), pp. 1817-1833, 1995.
http://dx.doi.org/10.1080/00207549508904783

[9] C.S. Cheng. A neural network approach for the analysis of control chart patterns. Int. J. Prod. Res., Vol. 35(3), pp. 667-697, 1997.
http://dx.doi.org/10.1080/002075497195650

[10] E.S. Ho and S. I. Chang. An integrated neural network approach for simultaneous monitoring of process mean and variance shifts - a comparative study. Int. J. Prod. Res., Vol. 37(8), pp. 1881-1901, 1999.
http://dx.doi.org/10.1080/002075499191049

[11] R.S. Guh and J.D.T. Tannock. Recognition of control chart concurrent patterns using a neural network approach. Int. J. Prod. Res., Vol. 37(8), pp. 1743-1765, 1999.
http://dx.doi.org/10.1080/002075499190987

[12] M.A. Wani, and D.T. Pham. Efficient control chart pattern recognition through synergistic and distributed artificial neural networks. Proc. Instn Mech. Engrs, Part B: J. Engineering Manufacture. Vol. 213(B2), pp. 157-169, 1999.
http://dx.doi.org/10.1243/0954405991517335

[13] D.F Cook, C.W. Zobel and M. L. Wolfe Environmental statistical process control using an augmented neural network classification approach. European Journal of Operational Research. Vol. 174, pp. 1631-1642, 2006.
http://dx.doi.org/10.1016/j.ejor.2005.04.035

[14] J.A. Swift. Development of a Knowledge-Based Expert System for Control Chart Pattem Recognition and Analysis. Doctoral Thesis. Oklahoma State University, 1987.

[15] Gerson Tontini. Pattern Identification in Statistical Process Control Using Fuzzy Neural Networks. IEEE Management Sciences. Department Regional University of Blumenau (FURB), 1996.

[16] G.A Carpenter, S. Grossberg, N. Markuzon, J.H. Reynolds, and D.B. Rosen. Fuzzy ARTMAP: A neural network architecture for incremental learning of analog multidimensional maps. IEEE Transactions on Neural Networks. Vol. 3 (5), pp. 698-713, 1992.
http://dx.doi.org/10.1109/72.159059

[17] G. A. Carpenter, S. Grossberg. Adaptive Resonance Theory (ART). The Hanbook of Brain Theory and Neural Networks. Edited by M. A. Arbib, The MIT Press. pp. 79-82, 1995.

[18] G. A. Carpenter, S. Grossberg. A Massively Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine. Computer Vision, Graphics, and Image Processing. Academic Press, Inc. pp. 54-115, 1987.

[19] G. A. Carpenter, S. Grossberg, D. B. Rosen. ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition. Neural Networks. Vol. 4, pp. 493-504, 1991.
http://dx.doi.org/10.1016/0893-6080(91)90045-7

[20] G. A. Carpenter, S. Grossberg, J. H. Reynolds. ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by Self-Organizing Neural Network. Neural Networks. pp. 565-588, 1991.
http://dx.doi.org/10.1016/0893-6080(91)90012-T

[21] J. R. Williamson. Gaussian ARTMAP: A Neural Network for Fast Incremental Learning of Noisy Multidimensional Maps. Neural Networks. Vol. 9, No. 5, pp. 881-897, 1996.
http://dx.doi.org/10.1016/0893-6080(95)00115-8

[22] G. A. Carpenter, W. D. Ross. ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation. IEEE Trans. on Neural Networks. Vol. 6, No. 4, pp. 805-818, 1995.
http://dx.doi.org/10.1109/72.392245

[23] D. Psichogios, L. Ungar. A Hybrid Neural Network-First Principles Approach to Process Modeling. Computers & Chemical Engineering. Vol. 38(10), pp. 1499-1511, 1992.
http://dx.doi.org/10.1002/aic.690381003

[24] G. Acu-a, and E. Pinto. Development of a MatlabrToolbox for the Design of Grey-Box Neural Models. Int. Journal of Computers, Communications & Control. Vol. I, No. 2, pp. 7-14, 2006.
http://dx.doi.org/10.15837/ijccc.2006.2.2280

[25] Imre J. Rudas, János Fodor. Intelligent Systems. Int. Journal of Computers, Communications & Control, 3(S):132-138, 2008.
Published
2010-06-01
How to Cite
VAZQUEZ-LOPEZ, J. A.; LOPEZ-JUAREZ, I.; PEÑA-CABRERA, M.. On the Use of the FuzzyARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 5, n. 2, p. 205-215, june 2010. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2475>. Date accessed: 05 july 2020. doi: https://doi.org/10.15837/ijccc.2010.2.2475.

Keywords

Statistical Process Control, Control Charts, Artificial Neural Network (ANN), FuzzyARTMAP, and Factorial Design