International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.6, No.10, Sep. 2014
Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach
Full Text (PDF, 486KB), PP.67-76
Traditionally, Control Chart Patterns (CCP) is widely used as a powerful method to measure, classify,analyze and interpret process data to improve the quality of products and service by detecting instabilities and justifying possible causes. In this study, we have developed an expert system that we called an expert system for control chart patterns recognition for recognition of the common types of control chart patterns (CCPs). The proposed system includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representation of CCPs. In the classifier module, the adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has a very important role for its recognition accuracy. Therefore, in the optimization module, imperialist competitive algorithm(ICA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.
Cite This Paper
Somayeh Mirzaei, Abdolhakim Nikpey, Payam Zarbakhsh,"Recognition of Control Chart Patterns Using Imperialist Competitive Algorithm and Fuzzy Rules Approach", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.10, pp.67-76, 2014. DOI: 10.5815/ijisa.2014.10.09
D. C. Montgomery, “Introduction to Statistical Quality Control,” 5thed., Hoboken, NJ, USA: John Wiley;2005.
L. S. Nelson, “The Shewhart control chart–test for special causes,” Journal of Quality Technology, Vol. 16, no. 4, pp. 237-239, 1984.
L. S. Nelson, “Interpreting Shewhart _X control chart,” Journal of Quality Technology, Vol. 17, no. 2, pp. 114-117, 1985.
J. A. Swift, and J. H. Mize, “Out-of-control pattern recognition and analysis for quality control charts using lisp-based systems,” Computers and Industrial Engineering, Vol. 28, pp. 81-91, 1995.
J. R. Evans, and W. M. Lindsay, “A framework for expert system development in statistical quality control,” Computers and Industrial Engineering, Vol. 14, no. 3, pp. 335-343, 1988.
D. T. Pham, and E. Oztemel, “XPC: An on-line expert system for statistical process control,” International Journal of Production Research, Vol. 30, no. 12, pp. 2857-2872, 1992.
Q. Le, X. Goal, L. Teng, and M. Zhu, “A new ANN model and its application in pattern recognition of control charts,” Proceedings ofInstitute of Electrical and Electronics Engineers, WCICA, pp. 1807-1811, 2008.
D. T. Pham, and M. A. Wani, “Feature-based control chart pattern recognition,” International Journal of Products Research, Vol. 35, no. 7, pp. 1875-1890, 1997.
Z. Cheng, and Y. Ma, “A research about pattern recognition of control chart using probability neural network,” Proceedings ofInternational Society for Eighteenth-Century Studies, pp. 140-145, 2008.
S. Sagiroujlu, E. Besdoc, and M. Erler, “Contro chart pattern recognition using artificial neural networks,” Turkish Journal of Electrical Engineering, Vol. 8, pp. 137-147, 2000.
A. A. Yousef, “Recognition of control chart patterns using multiresolution wavelets analysis and neural networks,” Computers and Industrial Engineering, Vol. 47, pp. 17-29, 2004.
R. S. Guh, and J. D. Tannock, “A neural network approach to characterize pattern parameters in process control charts,” Journal of Intelligent Manufacturing, Vol. 10, pp. 449-462, 1999.
S. K. Gauri, and S. Chakraborty, “A study on the various features for effective control chart pattern recognition,” International Journal of Advanced Manufacturing Technology, Vol. 34, pp. 385-398, 2007.
D. T. Pham, S. Otri, A. Ghanbarzadeh, and E. Koc, “Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition,”Wales, UK: Manufacturing Engineering Centre, Cardif University; 2006.
D. T. Pham, and E. Oztemel, “Control chart pattern recognition using linear vector quantization networks,” International Journal of Production Research, Vol. 32, pp. 721-729, 1994.
M. S. Yang, and J. H. Yang, “A fuzzy-soft learning vector quantization for control chart pattern recognition,” International Journal of Production Research, Vol. 40, no. 12, pp. 2721-2731, 2002.
C. H. Wang, W. Kuo, and H. Qi, “An integrated approach for process monitoring using wavelet analysis and competitive neural network,” International Journal of Production Research, Vol. 45, no. 1, pp. 227-244, 2007.
A. M. Al-Ghanim, and L. C. Ludeman, “Automated unnatural pattern recognition on control charts using correlation analysis techniques,” Computers and Industrial Engineering, Vol. 32, pp. 679-690, 1997.
J. H. Yang, and M. S. Yang, “A control chart pattern recognition scheme using a statistical correlation coefficient method,” Computers and Industrial Engineering, Vol. 48, pp. 205-221, 2005.
Z. Wu, G. Ren, X. Wang, and Y. Zhao, “Automatic digital modulation recognition using wavelet transform and neural networks,” Proceedings ofInternational Society of Nutrigenetics/Nutrigenomics, ISNN 2004, LNCS 3173, pp. 936-940, 2004.
M. Turk, and H. Ogras, “Classification of chaos-based digital modulation techniques using wavelet neural networks and performance comparison of wavelet families,” Expert Systems with Applications, Vol. 38, pp. 2557-2565, 2011.
E. Avci, D. Hanbay, and A. Varol, “An expert discrete wavelet adaptive network based fuzzy inference system for digital modulation recognition,” Expert Systems with Applications, Vol. 33, pp. 582-589, 2007.
M. Hosoz, H. M. Ertunc, and H. Bulgurcu,“An adaptive neuro-fuzzy inference system model for predicting the performance of a refrigeration system with a cooling tower,” Expert Systems with Applications, Vol. 38, pp. 14148-14155, 2011.
A. Keles, A. Keles, and U. Yavuz, “Expert system based on neuro-fuzzy rules for diagnosis breast cancer,” Expert Systems with Applications, Vol. 38, pp. 5719-5726, 2011.
S. A. Mallat, “A Wavelet Tour of Signal Processing,” New York: Academic Press; 1998.
E.H. Mamdani, S. Assilian, An experiment in linguistic synthesis with afuzzy logic controller, Int. J. Man-Mach. Stud. 1975; 7: 1–13.
J.S.R. Jang, ANFIS: Adaptive-Network-based Fuzzy Inference Systems, IEEE Trans. Syst., Man Cybern. 23 (May/June (3)) .1993; 665–685.
S. Haykin, Neural Networks—A Comprehensive Foundation, second ed.,Prentice-Hall of India Pvt. Ltd., New Delhi, India, 2003.
J.M. Zurada, Introduction to Artificial Neural Systems, PWS PublicationCompany, 1992.
M.T. Hagan, H.B. Demuth, M.H. Beale, Neural Network Design, PWSPublishing, Boston, MA, 1996.
S. Chiu, Fuzzy model identification based on cluster estimation, J. Intell.Fuzzy Syst. 1994; 2 (3) : 267–278.
S. Chiu, Selecting input variables for fuzzy models, J. Intell. Fuzzy Syst. 1996; 4(4): 243–256.
M. Buragohain , C. Mahanta. A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing 2008; 8: 609–625
E. Atashpaz-Gargari, C. Lucas. Designing an optimal PID controller using Colonial Competitive Algorithm. In: Proceedings of the First Iranian Joint Congress on Fuzzy and Intelligent Systems, Mashhad, Iran. 2007
E. Atashpaz-Gargari, C. Lucas. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore 2007; 4661–4667.
Available from: http://www.archive.ics.uci.edu/ml/ databases/synthetic control/synthetic control.data.html. last accessed date: 2011.
S. L. Salzberg, “On comparing classifiers: Pitfalls to avoid and a recommended approach,” Data Mining and Knowledge Discovery, Vol. 1, pp. 317-328, 1997.
K.S. Tang, K.F. Man, S. Kwong, Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine 13 (1996) 22–37.
J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks 4 (1995) 1942–1948.
M. Eusuff, K. Lansey, Optimization of water distribution network design using the shuffled frog leaping algorithm, Journal of Water Resource Plan and Management 129 (3) (2003) 10–25.
D. F. Specht, “Probabilistic neural networks,” Neural Networks, pp. 109-118, 1990.
T. Poggio, and F. Girosi, “Networks for approximation and learning,” Proceedings of the Institute of Electrical and Electronics Engineers, Vol. 78, pp. 1481-1497, 1990.
S. Haykin, “Neural Networks: A Comprehensive Foundation,” New York: MacMillan; 1999.
M. Riedmiller, and H. Braun, “A direct adaptive method for faster back propagation learning: The RPROP algorithm,” Proceedings of the Institute of Electrical and Electronics Engineers International Conference on Neural Networks, San Francisco, CA, March 28, pp. 586- 591, 1993.
S. Gauri a, S. Chakraborty. Recognition of control chart patterns using improved selection of features. Computers & Industrial Engineering 56 (2009) 1577–1588.
S. Gauri a, S. Chakraborty. Feature-based recognition of control chart patterns. Computers & Industrial Engineering 51 (2006) 726–742.
A. Hassan, M.S. Nabi Baksh, A.M. Shaharoun, H. Jamaluddin, Improved SPC chart pattern recognition using statistical features, International Journal of Production Research 41 (7) (2003) 1587–1603.
D. T. Pham, E. Oztemel, Control chart pattern recognition using neural networks, Journal of Systems Engineering 2 (1992) 256–262.