• DocumentCode
    1738136
  • Title

    Fuzzy regression: a genetic programming approach

  • Author

    Feuring, Thomas ; Golubski, Wolfgang ; Gassmann, Mike

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Siegen Univ., Germany
  • Volume
    1
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    349
  • Abstract
    Given some data pairs (X¯i, Y¯i), 1⩽i⩽k, of fuzzy numbers, we are interested in finding a fuzzy function F which best fits the given data. Because of fuzzy arithmetic, we cannot compute a fuzzy function with F(X¯i)=Y¯ i for all i, as in the crisp case. Therefore, we used a genetic programming approach to find a suitable fuzzy function. We present some tests and argue that this method is quite suitable for obtaining a fuzzy function which can explain the given data
  • Keywords
    arithmetic; function approximation; fuzzy set theory; genetic algorithms; mathematics computing; statistical analysis; fuzzy arithmetic; fuzzy function; fuzzy number data pairs; fuzzy regression; genetic programming; Arithmetic; Fuzzy sets; Fuzzy systems; Genetic algorithms; Genetic programming; Intelligent systems; Paper technology; Regression analysis; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on
  • Conference_Location
    Brighton
  • Print_ISBN
    0-7803-6400-7
  • Type

    conf

  • DOI
    10.1109/KES.2000.885828
  • Filename
    885828
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1738136