Identification of the Control Chart Patterns Using the Optimized Adaptive Neuro-Fuzzy Inference System

Автор: Abdolhakim Nikpey, Somayeh Mirzaei, Masoud Pourmandi, Jalil Addeh

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 7 vol.6, 2014 года.

Бесплатный доступ

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chart patterns (CCPs). The proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for finding of optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

Еще

ANFIS, Control chart patterns, Shape features, Statistical feature, COA

Короткий адрес: https://sciup.org/15014668

IDR: 15014668

Список литературы Identification of the Control Chart Patterns Using the Optimized Adaptive Neuro-Fuzzy Inference System

  • D.C. Montgomery. Introduction to Statistical Quality Control 2005. 5thed, John Wiley, Hoboken, NJ, USA.
  • J.A. Swift,J.H. Mize. Out-of-control pattern recognition and analysis for quality control charts using lisp-based systems. Computers and Industrial Engineering 1995; 28: 81–91.
  • J.R. Evans, W.M. Lindsay. A framework for expert system development in statistical quality control. Computers and Industrial Engineering 1998; 14: 335–343.
  • T.T. El-Midany. A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks. Expert Systems with Applications 37 (2010) 1035–1042.
  • S.M.T. Fatemi Ghomi.Recognition of unnatural patterns in process control charts through combining two types of neural networks. Applied Soft Computing 11 (2011) 5444–5456.
  • Z. Cheng. Y. Ma.A Research about Pattern Recognition of Control Chart Using Probability Neural Network. 2008 ISECS International Colloquium on Computing, Communication, Control, and Management.
  • R. S. Guh, Y. R. Shiue, Online identification of control chart patterns using self-organized approaches, International Journal of Production Research 2005;43: 1225–1254.
  • C.H. Wang, W. Kuo, H. Qi. An integrated approach for process monitoring using wavelet analysis and competitive neural network. International Journal of Production Research 2007; 45: 227–244.
  • K. Assaleh. Features extraction and analysis for classifying causable patterns in control charts. Computers & Industrial Engineering 49 (2005) 168–181.
  • D.T. Pham, M.A. Wani, Feature-based control pattern recognition, International Journal of Production Research 1997; 35: 1875--1890.
  • S.K. Gauri, S. Chakraborty. Improved recognition of control chart patterns using artificial neural networks, Int J Adv Manuf Technol 2008; 36: 1191- 1201.
  • Z. Chen, S. Lu, S. Lam. A hybrid system for SPC concurrent pattern recognition, Advanced engeering informatics 2007; 21: 303-310.
  • A. Hassan, M.S. Nabi Baksh, A.M. Shaharoun, H. Jamaluddin. Improved SPC chart pattern recognition using statistical features, International Journal of Production Research 2003; 41: 1587-1603.
  • S. Narayanamoorthy, S.Saranya, S.Maheswari. A Method for Solving Fuzzy Transportation Problem (FTP) using Fuzzy Russell's Method. International Journal of Intelligent Systems and Applications(IJISA). PP.71-75, Pub. Date: 2013-1-3.
  • Sakshi Bangia, P R Sharma, Maneesha Garg. Simulation of Fuzzy Logic Based Shunt Hybrid Active Filter for Power Quality Improvement. International Journal of Intelligent Systems and Applications(IJISA). PP.96-104, Pub. Date: 2013-1-3.
  • P.K Bhatia, Surender Singh. A New Measure of Fuzzy Directed Divergence and Its Application in Image Segmentation. International Journal of Intelligent Systems and Applications(IJISA). PP.81-89, Pub. Date: 2013-3-1.
  • E. Avci, D. Hanbay, A. Varol. An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition. Expert Systems with Applications 2007; 33: 582–589.
  • M. Hosoz, H.M. Ertunc, 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 2011; 38: 14148–14155.
  • M. Sugeno, G.T. Kang, Structure identification of fuzzy model, Fuzzy Sets Syst. 1988; 28: 15–33.
  • T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trns. Syst., Man Cybern 1985; 15: 116–132.
  • T. Takagi, M. Sugeno, Derivation of fuzzy control rules from humanoperator’s control actions, in: Proc. IFAC Symp. Fuzzy Inform.,Knowledge Representation and Decision Analysis, July 1985; 55–60.
  • 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.
  • R. Rajabioun. Cuckoo Optimization Algorithm. Applied Soft Computing 2011; 11: 5508–5518.
  • http://archive.ics.uci.edu/ml/databases/synthetic control/synthetic control. data.html.
  • D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing 2008; 8: 687–697.
  • K.S. Tang, K.F. Man, S. Kwong, Q. He, Genetic algorithms and their applications, IEEE Signal Processing Magazine 1996; 13: 22–37.
  • J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks 1995; 4:1942–1948.
  • R. Rajabioun, F. Hashemzadeh, E. Atashpaz-Gargari. Colonial competitive algorithm A novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics 2008; 3: 337–355.
  • D.F. Specht, Probabilistic neural networks, Neural Networks 1990; 109–118.
  • S.Haykin, Neural Networks: A Comprehensive Foundation, Mac Millan, New York, 1999.
  • M. Riedmiller, H. Braun, A direct adaptive method for faster back propagation learning: the RPROP algorithm, in: Proceedings of the IEEE Int. Conf. On Neural Networks, SanFrancisco, CA, March28, 1993.
  • D. T. Pham, E. Oztemel, Control chart pattern recognition using neural networks, Journal of Systems Engineering 1992; 2: 256–262.
  • D. T. Pham, E. Oztemel, Control chart pattern recognition using linear vector quantization networks, International Journal of Production Research 1994; 256–262.
  • R.S. Guh, J.D.T. Tannock. A neural network approach to characterize pattern parameters in process control.
  • S. Sagiroujlu, E. Besdoc, M. Erler, Contro chart pattern recognition using artificial neural networks, Turkish Journal of Electrical Engineering 2000; 8: 137–147.
  • S. Gauri a, S. Chakraborty. Feature-based recognition of control chart patterns. Computers & Industrial Engineering 2006; 51: 726–742.
  • Q. Le, X. Goal, L. Teng, M. Zhu, A new ANN model and its application in pattern recognition of control charts, in: Proc. IEEE. WCICA, 2008; 1807–1811.
  • Z. Cheng, Y. Ma, A research about pattern recognition of control chart using probability neural network, in: Proc. ISECS, 2008; 140–145.
  • S. Gauri a, S. Chakraborty. Recognition of control chart patterns using improved selection of features. Computers & Industrial Engineering 2009; 56: 1577–1588.
Еще
Статья научная