The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
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Optimization of Fuzzy Inference Systems Based on Data Information Granulation

데이터 정보입자 기반 퍼지 추론 시스템의 최적화

  • 오성권 (원광대학 전기전자공학부) ;
  • 박건준 (원광대학 전기전자공학부) ;
  • 이동윤 (중부대학 정보통신공학부)
  • Published : 2004.06.01
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Abstract

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

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, 'An identification algorithm in fuzzy relational system,' Fuzzy Sets Syst., Vol. 13, pp. 153-167, 1984 https://doi.org/10.1016/0165-0114(84)90015-0
  3. 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
  4. E. Czogola and W. Pedrycz, 'On identification in fuzzy systems and its applications in control problems,' Fuzzy Sets Syst., Vol. 6, pp. 73-83, 1981 https://doi.org/10.1016/0165-0114(81)90081-6
  5. C. W. Xu, 'Fuzzy system identification,' IEEE Proceeding Vol. 126, No.4, pp. 146-150, 1989
  6. R. M. Tong, 'The evaluation of fuzzy models derived from experimental data,' Fuzzy Sets Syst., Vol. 13, pp. 1-12, 1980 https://doi.org/10.1016/0165-0114(80)90059-7
  7. C. W. Xu and Y. Zailu, 'Fuzzy model identification self-learning for dynamic system,' IEEE Trans. on Syst, Man, Cybem., Vol. SMC-17, No.4, pp. 683-689, 1987 https://doi.org/10.1109/TSMC.1987.289361
  8. 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
  9. 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
  10. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, 2nd ed . Holden-Day, San Francisco, CA, 1976
  11. S.-K. Oh, 'Fuzzy Identification by Means of an Auto-Tuning Algorithm and a Weighted Performance Index,' Jorunal of Fuzzy Logic and Intelligent Systems, Vol. 8, No.6, pp. 106-118, 1998
  12. M. Sugeno and T. Yasukawa, 'A FuzzyLogic-Based Approach to Qualitative Modeling,' IEEE Trans. on Fuzzy systems, Vol. 1, No.1, pp. 7-13, 1993 https://doi.org/10.1109/TFUZZ.1993.390281
  13. 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, pp. 179-188, 1999 https://doi.org/10.1016/S0165-0114(97)00276-5
  14. E.-T. Kim, M.-K. Park, S.-H. Ji, M.-N. Park, 'A new approach to fuzzy modeling,' IEEE Trans. on Fuzzy systems, Vol. 5, No.3, pp. 328-337, 1997 https://doi.org/10.1109/91.618271
  15. E.-T Kim, H.-J Lee, M.-K. Park, M.-N. Park, 'a simply identified Sugeno-type fuzzy model via double clustering,' Information Sciences, Vol. 110, pp. 25-39, 1998 https://doi.org/10.1016/S0020-0255(97)10083-4
  16. S.-K. Oh, W. Pedrycz and B.- J Park, 'Polynomial Neural Networks Architecture: Analysis and Design,' Computers and Electrical Engineering, Vol. 29, Issue 6, pp. 703-725, 2003 https://doi.org/10.1016/S0045-7906(02)00045-9
  17. B.- J Park, W. Pedrycz and S.-K. Oh, 'Fuzzy Polynomial Neural Networks: Hybrid Architectures of Fuzzy Modeling,' IEEE Trans. on Fuzzy Systems, Vol. 10, No.5, pp 607-621, Oct. 2002 https://doi.org/10.1109/TFUZZ.2002.803495
  18. M. Sugeno, T. Yasukawa, ' Linguistic modeling based on numerical data,' IFSA'91 Brussels, Copmuter, Management & System Science, pp. 264-267, 1991
  19. 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 Syst., Vol. 115, No.2, pp. 205-230, 2000 https://doi.org/10.1016/S0165-0114(98)00174-2
  20. C.-S. Park, S.-K. Oh, and W. Pedrycz, 'Fuzzy Identification by means of Auto-Tuning Algorithm and Weighting Factor,' The Third Asian Fuzzy Systems Symposium(AFSS), pp. 701-706, 1998
  21. B. - J. Park, W. Pedrycz and S. - K. Oh, 'Identification of Fuzzy Models with the Aid of Evolutionary Data Granulation,' IEE Proc.-Control Theory and Applications, Vol. 148, Issue 05, pp, 406-418, Sept. 2001 https://doi.org/10.1049/ip-cta:20010677
  22. L. A. Zadeh, 'Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic,' Fuzzy Sets and Syst., Vol. 90, pp. 111-117, 1997 https://doi.org/10.1016/S0165-0114(97)00077-8
  23. W. Pderycz and G. Vukovich, 'Granular neural networks,' Neurocomputing, Vol. 36, pp. 205-224, 2001 https://doi.org/10.1016/S0925-2312(00)00342-8
  24. P. R. Krishnaiah and L. N. Kanal, editors. Classification, pattern recognition, and reduction of dimensionality, volume 2 of Handbook of Statistics. North-Holland, Amsterdam, 1982
  25. 오성권, '프로그래밍에 의한 컴퓨터 지능(퍼지, 신경회로망 및 진화알고리즘을 중심으로),' 내하출판사, 2002