The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Design of Type-2 FCM-based Fuzzy Inference Systems and Its Optimization

Type-2 FCM 기반 퍼지 추론 시스템의 설계 및 최적화

  • 박건준 (원광대 공대 정보통신공학과) ;
  • 김용갑 (원광대 공대 정보통신공학과) ;
  • 오성권 (수원대 공대 전기공학과)
  • Received : 2011.01.06
  • Accepted : 2011.07.08
  • Published : 2011.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.

Keywords

References

  1. R.M. Tong, "Synthesis of fuzzy models for industrial processes," Int. J. Gen. Syst., Vol. 4, pp. 143-162, 1978. https://doi.org/10.1080/03081077808960680
  2. W. Pedrycz, "Numerical and application aspects of fuzzy relational equations," Fuzzy Sets Syst., Vol. 11, pp. 1-18, 1983. https://doi.org/10.1016/S0165-0114(83)80066-9
  3. R. M. Tong, "The evaluation of fuzzy models derived from experimental data," Fuzzy Sets Syst., Vol. 13, pp. 1-12, 1980.
  4. C. W. Xu, "Fuzzy system identification," IEEE Proceeding, Vol. 126, No. 4, pp. 146-150, 1989.
  5. C. W. Xu and Y. Zailu, "Fuzzy model identification self-learning for dynamic system," IEEE Trans. on Syst. Man, Cybern., Vol. SMC-17, No. 4, pp. 683-689, 1987.
  6. T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst. Cybern., Vol.SMC-15, No. 1, pp. 116-132, 1985. https://doi.org/10.1109/TSMC.1985.6313399
  7. M. A. Ismail, "Soft Clustering Algorithm and Validity of Solutions," Fuzzy Computing Theory, Hardware and Applications, edited by M.M. Gupta, North Holland, pp. 445-471, 1988.
  8. L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning-I," Information Science, vol. 8, pp. 199-249, 1975. https://doi.org/10.1016/0020-0255(75)90036-5
  9. Mizumoto, M. and K. Tanaka, "Some Properties of Fuzzy Sets of Type-2," Information and Control, vol. 31, pp. 312-340, 1976 https://doi.org/10.1016/S0019-9958(76)80011-3
  10. J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall: NJ, 2001.
  11. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, PlenumPress, New York, 1981.
  12. 진강규, 유전알고리즘과 그 응용, 교우사, 2004.
  13. M. C. Mackey and L. Glass, "Oscillation and chaos in physiological control systems," Science, Vol.197, pp. 287-289, 1977. https://doi.org/10.1126/science.267326
  14. L. X. Wang and J. M. Mendel, "Generating fuzzy rules from numerical data with applications," IEEE Trans. System, Man, and Cybern., Vol. 22, No. 6, pp. 1414-1427, 1992. https://doi.org/10.1109/21.199466
  15. R. S. Crowder III, "Predicting the Mackey-Glass time series with cascade-correlation learning," In D. Touretzky, G. Hinton, and T. Sejnowski(Eds.), Proceedings of the 1990 Connectionist Models Summer School, pp. 117-123, Carnegic Mellon University, 1990.
  16. J. S. R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System," IEEE Trans. System, Man, and Cybern., Vol. 23, No. 3, pp. 665-685, 1993. https://doi.org/10.1109/21.256541
  17. L. P. Maguire, B. Roche, T. M. McGinnity, and L. J. McDaid, "Predicting a chaotic time series using a fuzzy neural network," Information Sciences, Vol. 112, pp. 125-136, 1998. https://doi.org/10.1016/S0020-0255(98)10026-9
  18. D. Kukolj, "Design of adaptive Takagi-Sugeno-Kang fuzzy models," Applied Soft Computing, Vol. 2, pp. 89-103, 2002. https://doi.org/10.1016/S1568-4946(02)00032-7
  19. P. Angelov and R. Buswell, "Identification of Evolving Fuzzy Rule-Based Models," IEEE Trans. Fuzzy Systems, Vol. 10, No. 5, pp. 667-677, 2002. https://doi.org/10.1109/TFUZZ.2002.803499
  20. Q. Song and N. K. Kasabov, "NFI: A Neuro-Fuzzy Inference Method for Transductive Reasoning," IEEE Trans. Fuzzy Systems, Vol. 13, No. 6, Dec. 2005.
  21. G. Vachtsevanos, V. Ramani, and T. W. Hwang, "Prediction of Gas Turbine NOx Emissions using Polynomial Neural Network," Technical Report, Georgia Institute of Technology, Atlanta, 1995.
  22. S. K. Oh, W. Pedrycz, and H. S. Park, "Hybrid Identification in Fuzzy-Neural Networks," Fuzzy Sets and Syst.,Vol. 138, pp. 399-426, 2003. https://doi.org/10.1016/S0165-0114(02)00441-4
  23. H. S. Park and S. K. Oh, "Multi-FNN Identification Based on HCM Clustering and Evolutionary Fuzzy Granulation," International Journal of Control, Automations, and Systems, Vol. 1, No. 2, pp. 194-202, June, 2003.
  24. H. S. Park and S. K. Oh, "Fuzzy Relation-based Fuzzy Neural Networks Using a Hybrid Identification Algorithm," International Journal of Control, Automation, and Systems, Vol. 1, No. 3, pp. 289-300, 2003.