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Fuzzy Rule Based Models and Approximate Reasoning

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Fuzzy Systems

Part of the book series: The Springer Handbook Series on Fuzzy Sets ((FSHS,volume 2))

Abstract

Fuzzy systems models form a special class of systems models that use the apparatus of fuzzy logic to represent the essential features of a system. From a formal point of view, fuzzy systems can be regarded as one alternative to the linear, nonlinear and neural modeling paradigms. Fuzzy systems models, however, possess a unique characteristic that is not available in most other types of formal modeling techniques — this is the ability to mimic the mechanism of approximate reasoning performed in the human mind. The most common fuzzy systems models consist of collections of logical IF — THEN rules with vague predicates; these rules along with the reasoning mechanism are the kernel of a fuzzy model.

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References

  1. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Systems, Man, and Cybernetics, 3, 28–44.

    Article  MathSciNet  MATH  Google Scholar 

  2. Sugeno, M. and Takagi, T. (1983). A new approach to design of fuzzy controller, In Advances in Fuzzy Sets, Possibility Theory and Applications, Wang, P.P. (Ed.), Plenum Press, New York, pp. 325–334.

    Chapter  Google Scholar 

  3. Takagi, T. and Sugeno, M. (1983) Derivation of fuzzy control rules from human operators actions, Proceedings of the IFAC Symposium on Fuzzy Information, Marseille, pp. 55–60.

    Google Scholar 

  4. Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man and Cybernetics, 15, 116–132.

    Article  MATH  Google Scholar 

  5. Sugeno, M. and Kang, G. T. (1986). Fuzzy modeling and control of multilayer incinerator, Fuzzy Sets and Systems, 18, 329–346.

    Article  MATH  Google Scholar 

  6. Zadeh, L. A. (1979). A theory of approximate reasoning, in Machine Intelligence, Vol. 9, Hayes, J., Michie, D., and Mikulich, L.I. (eds.), Halstead Press, New York, pp. 149–194.

    Google Scholar 

  7. Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant, Proceedings IEEE 121, pp. 1585–1588.

    Google Scholar 

  8. Mamdani, E. H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, 7, 1–13.

    Article  MATH  Google Scholar 

  9. Mamdani, E. H. and Baaklini, N. (1975). Prescriptive method for deriving control policy in a fuzzy logic control, Electronic Lett. Ii, 625–626.

    Google Scholar 

  10. Yager, R. R. and Filev, D. P. (1993). On the issue of defuzzification and selection based on a fuzzy set, Fuzzy Sets and Systems, 55, 255–272.

    Article  MathSciNet  MATH  Google Scholar 

  11. Mizumoto, M. (1991). Min-max-gravity method versus product-sum-gravity method for fuzzy controls, Proceedings Fourth IFSA Congress, Brussels, Engineering Part, pp. 127–130.

    Google Scholar 

  12. Gupta, M. M., Kiszka, J. B. and Trojan, G. J. (1986). Multivariate structure of fuzzy control systems, IEEE Transactions on Systems, Man and Cybernetics, 16, 638–656.

    Article  MATH  Google Scholar 

  13. Trojan, G. J., Kiszka, J. B., Gupta, M. M. and Nikiforuk, P. N., (1987). Solution of multivariable fuzzy equations, Fuzzy Sets and Systems 22.

    Google Scholar 

  14. Rajbman, N. S. (1981). Extensions to nonlinear and minimax approaches, in Trends and Progress in Systems Identification, Eykhoff, P. (Ed.), Pergamon Press, Oxford, pp. 196–213.

    Google Scholar 

  15. Yager, R. R. (1994). Aggregation operators and fuzzy systems modeling, Fuzzy Sets and Systems, 67, 129–146.

    Article  MathSciNet  MATH  Google Scholar 

  16. Yager, R. R. (1993). MAM and MOM operators for Aggregation, Information Sciences, 69, 259–273.

    Article  MATH  Google Scholar 

  17. Yager, R. R. (1993). Toward a unified approach to Aggregation in fuzzy and neural systems, Proceedings World Conference on Neural Networks, Portland, Volume II, pp. 619–622.

    Google Scholar 

  18. Yager, R. R. (1995). A unified approach to Aggregation based upon MOM and MAM operators, International Journal of Intelligent Systems, 10, 809–855.

    Article  Google Scholar 

  19. Yager, R. R. (1990). Using sets as a basis for knowledge representation, Int. J. of Expert Systems: Research and Applications, 3, 147–168.

    Google Scholar 

  20. Yager, R. R. and Filev, D. P. (1994). Essentials of Fuzzy Modeling and Control, J. Wiley, New York.

    Google Scholar 

  21. Gupta, M. M., Trojan, G. J. and Kiszka, J. B. (1986). Controllability of fuzzy control systems, IEEE Transactions on Systems, Man and Cybernetics, 16, 576–582.

    Article  MATH  Google Scholar 

  22. Pedrycz, W. (1984). Identification in fuzzy systems, IEEE Trans. on Systems, Man and Cybernetics, 14, 361–366.

    Article  MathSciNet  MATH  Google Scholar 

  23. Pedrycz, W. (1989). Fuzzy Control and Fuzzy Systems, J. Wiley, New York.

    MATH  Google Scholar 

  24. Czogala, E. and Pedrycz, W. (1982). Control problems in fuzzy systems, Fuzzy Sets and Systems, 7, 257–274.

    Article  MathSciNet  MATH  Google Scholar 

  25. Tong, R. M. (1980). Some properties of fuzzy feedback systems, IEEE Transactions on Systems, Man and Cybernetics, 10, 327–331.

    Article  MATH  Google Scholar 

  26. Sugeno, M. and Yasukawa, T. (1993). A fuzzy-logic based approach to qualitative modeling, IEEE Transactions on Fuzzy Systems, 1, 7–31.

    Article  Google Scholar 

  27. Tong, R. M. (1978). Synthesis of fuzzy models for industrial processessome recent results, Int. J. of General Systems, 4, 143–163.

    Article  MATH  Google Scholar 

  28. Tong, R. M. (1979). The construction and evaluation of fuzzy models, in Advances in Fuzzy Set Theory and Applications, Gupta, M. M., Ragade, R. K. and Yager, R. R. (Eds.), North-Holland, Amsterdam, pp. 559–576.

    Google Scholar 

  29. Tong, R. M., Beck, M. B. and Latten, A. (1980). Fuzzy control of the activated sludge waste water treatment process, Automatica, 16, 695–702.

    Article  MATH  Google Scholar 

  30. Franklin, G. F., Powell, J. D. and Workman, M. L. (1990). Digital Control of Dynamic Systems, Addison-Wesley, Reading, Ma.

    MATH  Google Scholar 

  31. Filev, D. P. (1992). System approach to dynamic fuzzy models, International Journal of General Systems, 21, 311–337.

    Article  MATH  Google Scholar 

  32. Haber, R. and Unbehauen, H. (1990). Structure identification of nonlinear dynamic systems-a survey on input-output approaches, Automatica, 26, 651–677.

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Machine Intelligence Institute, Iona College, New Rochelle, NY, 10801, USA

    Ronald R. Yager

  2. Ford Motor Company, 24500 Glendale Ave., Detroit, MI, 48239

    Dimitar P. Filev

Authors
  1. Ronald R. Yager
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  2. Dimitar P. Filev
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Editor information

Editors and Affiliations

  1. New Mexico State University, USA

    Hung T. Nguyen

  2. Tokyo Institute of Technology, Japan

    Michio Sugeno

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© 1998 Springer Science+Business Media New York

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Yager, R.R., Filev, D.P. (1998). Fuzzy Rule Based Models and Approximate Reasoning. In: Nguyen, H.T., Sugeno, M. (eds) Fuzzy Systems. The Springer Handbook Series on Fuzzy Sets, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5505-6_4

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  • DOI: https://doi.org/10.1007/978-1-4615-5505-6_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7515-9

  • Online ISBN: 978-1-4615-5505-6

  • eBook Packages: Springer Book Archive

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