Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Self-Organizing Fuzzy Modeling Based on Hyperplane-Shaped Clusters

다차원 평면 클러스터를 이용한 자기 구성 퍼지 모델링

  • Koh, Taek-Beom (Dept.of Computer Electronics Engineering, Gyeongju University)
  • 고택범 (경주대학교 컴퓨터전자공학부)
  • Published : 2001.12.01
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Abstract

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

Keywords

References

  1. -, 'Editorial: Fuzzy models-What are they and why?,' IEEE Trans. Fuzzy Syst., vol. 1, pp. 1-6, Feb., 1993 https://doi.org/10.1109/91.273124
  2. M. Sugeno and T. Yasukawa, 'A fuzzy-logic-based approach to qualitative modeling,' IEEE Trans. on Fuzzy Systems, vol. 1, no. 1, pp. 7-31, Feb., 1993 https://doi.org/10.1109/TFUZZ.1993.390281
  3. T. Takagi and M. Sugeno, 'Fuzzy identification of systems and its application to modeling and control,' IEEE Trans. on Syst., Man & Cybern., vol. 15, pp. 116-132, 1985
  4. C. L. Karr and E. J. Gentry, 'Fuzzy control of pH using genetic algorithms,' IEEE Trans. Fuzzy Systems, vol. 1, no. 1, pp. 46-53, Jan., 1993 https://doi.org/10.1109/TFUZZ.1993.390283
  5. A. Homaifar and E. McCormick, 'Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms,' IEEE Trans. Fuzzy Systems, vol. 3,no. 2, pp. 129-139, May, 1995 https://doi.org/10.1109/91.388168
  6. K. Tanaka and M. Sugeno, 'Stability analysis and design of fuzzy control systems,' Fuzzy Sets Syst., vol. 45, pp. 136-156, 1992 https://doi.org/10.1016/0165-0114(92)90113-I
  7. K. Tanaka and M. Sano, 'A robust stabilization problem of fuzzy control systems and its application to backing up control of a truck-trailer,' IEEE Trans. Fuzzy Syst., vol, 2, pp. 119-134, May, 1994 https://doi.org/10.1109/91.277961
  8. K. Tanaka, T. Ikeda, and H. O. Wang, 'Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: Quadratic stabilizability, $H{\infty}$ control theory, and linear matrix inequalities,' IEEE Trans, Fuzzy Syst., vol. 4, pp. 1-13, Feb., 1996 https://doi.org/10.1109/91.481840
  9. E. Kim, M. Park, S. Ji, and M. Park, 'A new approach to fuzzy modeling,' IEEE Trans. Fuzzy Syst., vol. 5, no. 3, pp. 328-337, 1997 https://doi.org/10.1109/91.618271
  10. L. Ljung, System Identification: Theory for the User. Englewood Cliffs. NJ: Prentice-Hall, 1987
  11. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Functional Algorithm, New York: Plenum, 1981
  12. G. E. P. Box and G. M. Jenkins, Time Series Analysis, Forecasting and Control. San Francisco, CA: Holden Day, 1970
  13. M. Sugeno and K. Tanaka, 'Succesive identification of a fuzzy model and its applications to prediction of a complex system,' Fuzzy Sets Syst., vol. 42, pp. 315-334, 1991 https://doi.org/10.1016/0165-0114(91)90110-C
  14. W. Pedrycz, 'An identification algorithm in fuzzy relational systems,' Fuzzy Sets Syst., vol. 13, pp. 153-167, 1984 https://doi.org/10.1016/0165-0114(84)90015-0
  15. S. K. Oh and W. Pedrycz, 'Identification of fuzzy systems by means of an Auto-Tuning algorithm and its application to nonlinear systems,' Fuzzy Sets and Systems, vol. 115, no. 2, pp. 205-230, 2000 https://doi.org/10.1016/S0165-0114(98)00174-2
  16. Y. Lin and G. A. Cunningham III, 'A new approach to fuzzy-neural modeling,' IEEE Trans. Fuzzy Sets Syst., vol. 45, pp. 136-156, 1995 https://doi.org/10.1109/91.388173
  17. E. Kim, H. Lee, M. Park, and M. Park, 'A simple identified Sugeno-type fuzzy model via double clustering,' Information Sciences 110, 25-39, 1998 https://doi.org/10.1016/S0020-0255(97)10083-4
  18. A. F. Gomez-Skarmeta, M. Delgado, and M. A. Vila, 'About the use of fuzzy clustering techniques for fuzzy model identification,' Fuzzy Sets and Systems, vol. 106, 179-188, 1999 https://doi.org/10.1016/S0165-0114(97)00276-5
  19. S. -J. Kang, C. -H. Woo, H. -S. Hwang and K. B. Woo, 'Evolutionary design of fuzzy rule base for nonlinear system modeling and control,' IEEE Tram. Fuzzy Sets Syst., vol. 8, pp. 37-45, 2000 https://doi.org/10.1109/91.824766
  20. S. K. Oh, D. W. Kim, B. J. Park, and H. S. Hwang, 'Advanced polynomial neural networks architecture with new adaptive nodes,' Trans. on Control, Automation and Systems Engineering, vol. 3, no. 1, Mar., 2001
  21. B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs. NJ: Prentice-Hall, 1992
  22. 고택범, 이덕규, '감수분열 유전알고리즘을 이용한 퍼지 모델의 자동 설계,' 대한전기학회 학술회의 논문집, pp.2696-2698, 2000