Simple Modification of Takagi-Sugeno Model

  • Bohdan Butkiewicz
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)


The Takagi-Sugeno model is very popular in fuzzy logic area. It can approximate different functions and physical processes. However, it is known that the model has one inconvenience. If simple triangular or trapezoidal membership functions are used for fuzzy sets then in intermediate area, described example by two fuzzy sets (conclusions of two rules) and two approximate functions used in these rules, the model can not approximate the process well. In the paper simple modification of the model is presented as one giving very good result.


Membership Function Fuzzy System Approximate Function Intermediate Area Universal Approximators 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Babuska R., (2001) Construction of fuzzy systems - interplay between precision and transparency, European Symposium on Intelligent Technics (ESIT), Aachen, Germany, 445 - 452Google Scholar
  2. 2.
    Butkiewicz B. S., (2001) Inference in Fuzzy Models of Physical Processes, in Computational Intelligence Theory and Applications, Lecture Notes in Computer Science 2206, B. Reusch (ed.), Springer, Berlin, New York, 782 - 790Google Scholar
  3. 3.
    Kosko B., (1997) Fuzzy Engineering, Prentce-Hall Inc., LondonMATHGoogle Scholar
  4. 4.
    Sugeno M., Kang G. T., (1988) Structure Identification of Fuzzy Model, Fuzzy Sets and Systems, 28, 15 - 33MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Takagi T, Sugeno M., (1985) Fuzzy Identification of Systems and its Application to Modeling and Control, IEEE Trans. on Systems, Man, and Cybernetics, 15, No. 1, 116 - 132MATHCrossRefGoogle Scholar
  6. 6.
    Takagi T., Sugeno M., (1983) Derivation of fuzzy control rules form human operator’s control system, Proc. of IFAC Symp. on Fuzzy Information, Knowledge Representation and Decision Analysis, Marseilles, France, 55 - 60Google Scholar
  7. 7.
    Ying H., (1998) Sufficient condition on uniform approximation of multivariate functions by general Takagi-Sugeno systems, IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, 28, 515 - 520CrossRefGoogle Scholar
  8. 8.
    Wang X. L., (1992) Fuzzy systems are universal approximators, Proc IEEE Int. Conf. on Fuzzy systems, San Diego (CA), 1163 - 1169Google Scholar
  9. 9.
    Wang X. L., Mendel J. M., (1992) Fuzzy basis functions, universal approximation, and orthogonal least-square learning, IEEE Trans. on Neural Network, 3, No. 5, 807 - 814Google Scholar
  10. 10.
    Ying H., Ding Y., Li S., Shao S., (1999) Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy system as universal approximators, IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, 29, No. 5, 508 - 514CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Bohdan Butkiewicz
    • 1
  1. 1.Institute of Electronic SystemsWarsaw University of TechnologyWarsawPoland

Personalised recommendations