• DocumentCode
    2271748
  • Title

    Fuzzy least absolute deviations regression based on the ranking of fuzzy numbers

  • Author

    Chang, Ping-Teng ; Lee, E. Stanley

  • Author_Institution
    Dept. of Ind. Eng., Kansas State Univ., Manhattan, KS, USA
  • fYear
    1994
  • fDate
    26-29 Jun 1994
  • Firstpage
    1365
  • Abstract
    A general fuzzy linear model for fuzzy regression analysis was formulated. Based on both this general model and a fuzzy difference ranking method (P.-T. Chang and E.S. Lee, 1993; 1992), approaches for fuzzy least squares regression and fuzzy least absolute value deviations regression were proposed. The former approach resulted in a nonlinear programming problem while the latter resulted in a linear programming problem. Numerical examples were solved by using the absolute deviations approach to illustrate the problems of conflicting trends and ways to at least partially overcoming these problems. Furthermore, these examples showed that the absolute deviations formulation forms an effective computational tool
  • Keywords
    fuzzy systems; least squares approximations; linear programming; nonlinear programming; statistical analysis; computational tool; conflicting trends; fuzzy difference ranking method; fuzzy least absolute deviations regression; fuzzy least squares regression; fuzzy numbers; fuzzy regression analysis; general fuzzy linear model; linear programming problem; nonlinear programming problem; ranking; Arithmetic; Artificial intelligence; Equations; Fuzzy sets; Fuzzy systems; Industrial engineering; Least squares methods; Linear programming; Linear regression; Regression analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1896-X
  • Type

    conf

  • DOI
    10.1109/FUZZY.1994.343613
  • Filename
    343613