Evolutionary Design of Fuzzy Rule Base for Modeling and Control

비선형 시스템 모델링 및 제어를 위한 퍼지 규칙기반의 진화 설계

  • 이창훈 (한라대 전기전자제어공학부)
  • Published : 2001.12.01

Abstract

In designing fuzzy models and controllers, we encounter a major difficulty in the identification f an optimized fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. This paper presents an approach to the evolutionary design of an optimal fuzzy rule base for modeling and control. Evolutionary programming is used to simultaneously evolve the structure and the parameter of fuzzy rule base for a given task. To check the effectiveness of the suggested approach, four numerical examples are examined. The performance of the identified fuzzy rule bases is demonstrated.

Keywords

References

  1. H.S.Hwang and K.B.Woo, 'Linguistic fuzzy model identification,' IEE Proc. Part D, Vol. 142, pp. 537-544, Nov. 1995 https://doi.org/10.1049/ip-cta:19952254
  2. Y.H.Joo, H.S.Hwang, K.B.Kim, and K.B.Woo, 'Linguistic model identification for fuzzy system,' Electron. Lett., vol. 31, no. 4, pp. 330-331, Feb. 1995 https://doi.org/10.1049/el:19950163
  3. M.Sugeno and T.Yasukawa, 'A fuzzy-logic- based approach to qualitative modeling,' IEEE trans. Fuzzy Syst., vol. 1, pp. 7-31, Feb. 1993 https://doi.org/10.1109/TFUZZ.1993.390281
  4. T.Tagaki and M.Sugeno, 'Fuzzy identification of systems and its applications to modeling and control,' IEEE trans. Syst., Man, Cybern., vol. 15, pp. 116-132, 1985
  5. C.L.Karr and E.J.Gentry, 'Fuzzy control of pH using genetic algorithms,' IEEE Trans. Fuzzy Syst., vol. 1, pp. 46-53, Jan. 1993 https://doi.org/10.1109/TFUZZ.1993.390283
  6. A.Homaifar and E.McCormick, 'Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms,' IEEE Trans. Fuzzy Syst., vol. 3, pp. 129-139, May 1995 https://doi.org/10.1109/91.388168
  7. L.A.Zadeh, 'A theory of approximate reasoning,' in Machine Intelligence, L.I.Mikulich, J.E.Hayes, and D.Mitchie, Eds, New York: wiley, 1979, vol. 9
  8. L. A. Zadeh, 'Fuzzy sets,' Inform. Contr., vol. 8, pp. 338-353, 1965 https://doi.org/10.1016/S0019-9958(65)90241-X
  9. I.Rechenberg, 'Cybernetic solution path of an experimental problem,' Farnborough, Hants, Germany, Tech. Rep., 1995
  10. H. P. Schwefel, 'Evolutionsstrategie; Optimie- erung Technisher System Nach prinzipen Der Biologischen Evolution, Verlagm, Germany: Frommamm-Holzboog, 1973
  11. H.P.Schwefel, 'Evolutionsstrategie und nume- rische Optimierung,' Ph.D. dissertation, Tech. Univ. Berlin, Berlin, Germany, 1975
  12. J.H.Holland, Adaptation in Natural and Artifi- cial Systems. Ann Arbor, MI: Univ. Michigan Press, 1975
  13. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learnig. Reading, MA: Addison-Wesley, 1991
  14. L.J.Fogel, A.J.Owens, and M.J.Walsh, Artifi- cial Intelligence through simulated Evolution, New York, Wiley, 1966
  15. D.B.Fogel and J.W.Atmar, Proc. 1st Annu. Conf. Evolutionary Programming Evolutionary Programming Soc., La Jolla, CA, 1992
  16. G.E.P.Box and G.M.Jenkins, Time Series Analysis, Forecasting and Control, San Francisco, CA: Holden Day, 1970
  17. R. M. Tong, 'The evaluation of fuzzy models derived from experimental data,' Fuzzy Sets Syst., Vol. 4, pp. 1-12, 1980 https://doi.org/10.1016/0165-0114(80)90059-7
  18. 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
  19. C.W. XU, 'Fuzzy systems identification,' IEE Proc. D., Vo;. 136, No. 4, pp. 146-150, 1989
  20. R. Thawonmas, S. Abe, 'Function approximation based on fuzzy rules extracted from partitioned numerical data,' IEEE Trans. on Part B Systems, Man and Cybernetics, Vol. 29, Issue: 4, pp. 525-534, Aug. 1999 https://doi.org/10.1109/3477.775268
  21. W. E. Combs, J. E. Andrews, 'Combinatorial rule explosion eliminated by a fuzzy rule configuration,' IEEE Transactions on Fuzzy Systems, Vol.6, Issue: 1, pp. 1-11, Feb, 1998
  22. Hau-San Wong, Ling Guan, 'A neural learning approach for adaptive image restoration using a fuzzy model-based network architecture,' IEEE Transactions on Neural Networks, Vol. 12 Issue: 3, pp. 516-531, May 2001 https://doi.org/10.1109/72.925555
  23. Ji-Cheng Duan, Fu-Lai Chung, 'Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning,' IEEE Transactions on Fuzzy Systems, Vol. 9, Issue: 2, pp. 293-306, April 2001 https://doi.org/10.1109/91.919250