• DocumentCode
    845899
  • Title

    Comments on the benchmarks in "A proposal for improving the accuracy of Linguistic Modeling" and related articles

  • Author

    Roubos, J.A. ; Babuska, Robert

  • Author_Institution
    Delft Center for Syst. & Control, Delft Univ. of Technol., Netherlands
  • Volume
    11
  • Issue
    6
  • fYear
    2003
  • Firstpage
    861
  • Lastpage
    865
  • Abstract
    In the above paper by Cordon and Herrara (IEEE Trans. Fuzzy Syst., vol. 8, p. 335-44, 2000), the so-called accurate linguistic modeling (ALM) method was proposed to improve the accuracy of linguistic fuzzy models. A number of examples are given to demonstrate the benefits of the approach. We show that: 1) these examples are not suitable as benchmarks or demonstrators of nonlinear modeling techniques and 2) better results can be obtained by using both standard regression tools as well as other fuzzy modeling techniques. We argue that benchmark examples that are used in articles to demonstrate the effectiveness of fuzzy modeling techniques should be selected with great care. Critical analysis of the results should be made and linear models should be regarded as a lower bound on the acceptable performance.
  • Keywords
    fuzzy logic; learning (artificial intelligence); modelling; pattern clustering; polynomials; regression analysis; splines (mathematics); Takagi-Sugeno fuzzy model; accurate linguistic modeling method; benchmarks; demonstrators; fuzzy modeling techniques; linear models; linguistic fuzzy models; nonlinear modeling techniques; rice data problem; spline model; standard regression tools; Accuracy; Data analysis; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Linear regression; Neural networks; Performance analysis; Proposals; Spline;
  • fLanguage
    English
  • Journal_Title
    Fuzzy Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1063-6706
  • Type

    jour

  • DOI
    10.1109/TFUZZ.2003.819822
  • Filename
    1255421