Advertisement

Methodological Identification of Information Granules-Based Fuzzy Systems by Means of Genetic Optimization

  • Sung-Kwun Oh
  • Keon-Jun Park
  • Witold Pedrycz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)

Abstract

In this study, we introduce an information granules-based fuzzy systems and a methodological identification by means of genetic optimization to carry out the model identification of complex and nonlinear systems. Information granulation realized with Hard C-Means clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the 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. The design methodology emerges as a hybrid structural optimization and parametric optimization. Especially, genetic algorithms (GAs) and HCM clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and parameters of fuzzy model we exploit the methodologies of a respective and consecutive identification by means of genetic algorithms. The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

Keywords

Genetic Algorithm Membership Function Fuzzy Rule Fuzzy Model Consequence Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Tong, R.M.: Synthesis of fuzzy models for industrial processes. Int. J. Gen. Syst. 4, 143–162 (1978)MATHCrossRefGoogle Scholar
  3. 3.
    Pedrycz, W.: An identification algorithm in fuzzy relational system. Fuzzy Sets Syst. 13, 153–167 (1984)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Pedrycz, W.: Numerical and application aspects of fuzzy relational equations. Fuzzy Sets Syst. 11, 1–18 (1983)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Czogola, E., Pedrycz, W.: On identification in fuzzy systems and its applications in control problems. Fuzzy Sets Syst. 6, 73–83 (1981)CrossRefGoogle Scholar
  6. 6.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst, Cybern. SMC 15(1), 116–132 (1985)MATHGoogle Scholar
  7. 7.
    Sugeno, M., Yasukawa, T.: Linguistic modeling based on numerical data. In: IFSA 1991 Brussels, Computer, Management & System Science, pp. 264–267 (1991)Google Scholar
  8. 8.
    Ismail, M.A.: Soft Clustering Algorithm and Validity of Solutions. In: Gupta, M.M. (ed.) Fuzzy Computing Theory, Hardware and Application, pp. 445–471. North-Holland, Amsterdam (1988)Google Scholar
  9. 9.
    Oh, S.K., Pedrycz, W.: Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems. Fuzzy Sets and Syst 115(2), 205–230 (2000)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Syst. 90, 111–117 (1997)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Pderycz, W., Vukovich, G.: Granular neural networks. Neurocomputing 36, 205–224 (2001)CrossRefGoogle Scholar
  12. 12.
    Krishnaiah, P.R., Kanal, L.N. (eds.): Classification, pattern recognition, and reduction of dimensionality. Handbook of Statistics, vol. 2. North-Holland, Amsterdam (1982)Google Scholar
  13. 13.
    Golderg, D.E.: Genetic Algorithm in search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)Google Scholar
  14. 14.
    Tong, R.M.: The evaluation of fuzzy models derived from experimental data. Fuzzy Sets Syst. 13, 1–12 (1980)CrossRefGoogle Scholar
  15. 15.
    Xu, C.W., Zailu, Y.: Fuzzy model identification self-learning for dynamic system. IEEE Trans. on Syst. Man, Cybern. SMC 17(4), 683–689 (1987)MATHCrossRefGoogle Scholar
  16. 16.
    Park, C.S., Oh, S.K., Pedrycz, W.: Fuzzy Identification by means of Auto-Tuning Algorithm and Weighting Factor. In: The Third Asian Fuzzy Systems Symposium (AFSS), pp. 701–706 (1998)Google Scholar
  17. 17.
    Park, B.J., Pedrycz, W., Oh, S.K.: Identification of Fuzzy Models with the Aid of Evolutionary Data Granulation. IEE Proc.-Control Theory and Applications 148(05), 406–418 (2001)CrossRefGoogle Scholar
  18. 18.
    Park, H.S., Oh, S.K.: Fuzzy Relation-based Fuzzy Neural-Networks Using a Hybrid Identification Algorithm. IJCAS 1(3), 289–300 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sung-Kwun Oh
    • 1
  • Keon-Jun Park
    • 1
  • Witold Pedrycz
    • 2
    • 3
  1. 1.Department of Electrical EngineeringThe University of SuwonHwaseong-si, Gyeonggi-doSouth Korea
  2. 2.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  3. 3.Systems Research InstitutePolish Academy of SciencesWarsawPoland

Personalised recommendations