Fuzzy Models: Methodology, Design, Applications and Challenges

  • Witold Pedrycz
Part of the International Series in Intelligent Technologies book series (ISIT, volume 7)


The essence of fuzzy modelling is concerned with constructing models that flexibly cope with heterogeneous data including those of linguistic and numerical character. In this study, we concentrate on the methodological principles guiding the development of fuzzy models, discuss their general topology and elaborate on selected algorithmic aspects. We also address several main design issues that are aimed at achieving information flexibility and versatility of the fuzzy models.


Fuzzy Number Fuzzy System Fuzzy Model Linguistic Term Fuzzy Neural Network 
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.


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  1. 1.
    J.C. Bezdek, Fuzzy models — what are they, and why?, IEEE Trans, on Fuzzy Systems, 1, 1993, 1–6.Google Scholar
  2. 2.
    A. Di Nola, S. Sessa, W. Pedrycz, E. Sanchez, Fuzzy Relational Equations and Their Applications in Knowledge Engineering, Kluwer Academic Press, Dordrecht, 1989.Google Scholar
  3. 3.
    D. Dubois, H. Prade, Possibility Theory - An Approach to Computerized Processing of Uncertainty, Plenum Press, New York, 1988.zbMATHGoogle Scholar
  4. 4.
    B. Heshmaty, A. Kandel, Fuzzy linear regression and its applications to forecasting in uncertain environment, Fuzzy Sets and Systems, 15, 1985, 159–191.zbMATHCrossRefGoogle Scholar
  5. 5.
    A. Kandel, Fuzzy Techniques in Pattern Recognition, J. Wiley, New York, 1982.zbMATHGoogle Scholar
  6. 6.
    E. T. Lee, L. A. Zadeh, Note on fuzzy languages, Information Sciences, 1, 1969, 421–434.MathSciNetCrossRefGoogle Scholar
  7. 7.
    M. Mizumoto, J. Toyoda, K. Tanaka, General formulation of formal grammars, Information Sciences, 4, 1972, 87–100.MathSciNetGoogle Scholar
  8. 8.
    W. Pedrycz, Processing in relational structures: fuzzy relational equations, Fuzzy Sets and Systems, 40, 1990, 77–106.MathSciNetCrossRefGoogle Scholar
  9. 9.
    W. Pedrycz, Neurocomputations in relational systems, IEEE Trans, on Pattern Analysis and Machine Intelligence, 13, 1991, 289–296.CrossRefGoogle Scholar
  10. 10.
    W. Pedrycz, Selected issues of frame of knowledge representation realized by means of linguistic labels, Int. J.of Intelligent Systems, 7, 1992, 155–170.CrossRefGoogle Scholar
  11. 11.
    W. Pedrycz, Fuzzy Control and Fuzzy Systems, 2nd extended edition, Research Studies Press/J.Wiley, Taunton/New York, 1993.zbMATHGoogle Scholar
  12. 12.
    W. Pedrycz, Fuzzy neural networks and neurocomputations, Fuzzy Sets and Systems, 56, 1993, 1–28.CrossRefGoogle Scholar
  13. 13.
    W. Pedrycz, Fuzzy Sets Engineering, CRC Press, Boca Raton, 1995.zbMATHGoogle Scholar
  14. 14.
    W. Pedrycz, J. Valente de Oliveira, Optimization of fuzzy models, IEEE Trans. on Systems, Man, and Cybernetics, to appear.Google Scholar
  15. 15.
    W. Pedrycz, J. Valente de Oliveira, Optimization of fuzzy relational models, Proc. 5th IFSA World Congress, Seoul, 1993, pp. 1187–1190.Google Scholar
  16. 16.
    E. Sanchez, Resolution of composite fuzzy relation equations, Information and Control, 30, 1976, 38–47.MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    E. S. Santos, Context - free fuzzy languages, Information and Control, 26, 1974, 1–11.MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    M. Sugeno (ed.), Industrial Applications of Fuzzy Control, North Holland, Amsterdam, 1985.Google Scholar
  19. 19.
    M. Sugeno, T. Yasukawa, A fuzzy-logic-based approach to qualitative modeling, IEEE Trans, on Fuzzy Systems, 1, 1993, 7–31.CrossRefGoogle Scholar
  20. 20.
    T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Trans, on Systems, Man, and Cybernetics, 15, 1985, 116–132.zbMATHGoogle Scholar
  21. 21.
    H. Tanaka, S. Uejima, K. Asai, Linear regression analysis with fuzzy model, IEEE Trans, on Systems, Man, and Cybernetics, 12, 1982, 903–907.zbMATHCrossRefGoogle Scholar
  22. 22.
    J. Valente de Oliveira, On optimal fuzzy systems with I/O interfaces, Proc. 2nd Int. Conf. on Fuzzy Systems, San Francisco, 1993.Google Scholar
  23. 23.
    J. Valente de Oliveira, A design methodology for fuzzy systems interfaces, IEEE Trans, on Fuzzy Systems, to appear.Google Scholar
  24. 24.
    L.A. Zadeh, Fuzzy sets and systems, Proc. Symp. Syst. Theory Polytech. Inst. Brooklyn, 1965, 29–37.Google Scholar
  25. 25.
    L.A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 1, 1978, 3–28.MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    L. A. Zadeh, Fuzzy sets and information granularity, in: M.M. Gupta, R.K. Ragade, R.R. Yager, eds., Advances in Fuzzy Set Theory and Applications, North Holland, Amsterdam, 3–18, 1979.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Witold Pedrycz
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of ManitobaWinnipegCanada

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