International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Information Granulation-based Fuzzy Inference Systems by Means of Genetic Optimization and Polynomial Fuzzy Inference Method

  • Park Keon-Jun (Department of Electrical Engineering, The University of Suwon) ;
  • Lee Young-Il (Department of Electrical Engineering, The University of Suwon) ;
  • Oh Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
  • Published : 2005.09.01
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Abstract

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

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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 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. C. W. Xu, 'Fuzzy system identification,' IEE Proceeding, Vol. 126, Issue. 4, pp.146-150, 1989
  7. 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(84)90022-8
  8. M. Sugeno, T. Yasukawa, 'Linguistic modeling based on numerical data.', In: IFSA91 Brussels, Computer, Management & System Science., pp.264-267, 1991
  9. 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
  10. 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
  11. 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
  12. L. A. Zadeh, 'Fuzzy sets and information granularity,' in Advances in Fuzzy Set Theory and Applications, M. M. Gupta, R. K. Ragade, and R. R. Yager, Eds. Amsterdam, The Netherlands: North Holland, pp. 3-18, 1979
  13. 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
  14. 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
  15. D. E. Golderg, 'Genetic Algorithm in search, Optimization & Machine Learning,' Addison wesley, 1989
  16. M.C. Mackey and L. Glass, 'Oscillation and chaos in physiological control systems,' Science, 197, pp 287-289, July 1977 https://doi.org/10.1126/science.267326
  17. L. X. Wang, J. M. Mendel, 'Generating fuzzy rules from numerical data with applications,' IEEE Trans. Systems, Man, Cybern., Vol. 22, No. 6, pp. 1414-1427, 1992 https://doi.org/10.1109/21.199466
  18. R. S. Crowder III,,'Predicting the Mackey-Glass time series with cascade-correlation learning,' In D. Touretzky, G. Hinton, and T. Sejnowski, editors, Proceedings of the 1990 Connectionist Models Summer School, pp. 117-123, Carnegic Mellon University, 1990
  19. 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
  20. L. P. Maguire, B. Roche, T. M. McGinnity, 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
  21. C. James Li, T. -Y. Huang, 'Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks,' Applied Mathematical Modeling, Vol. 23, pp. 933-944, 1999 https://doi.org/10.1016/S0307-904X(99)00020-7