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Possibilistic regression analysis

  • Hideo Tanaka
  • Peijun Guo
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 87)

Abstract

In this paper two possibilitis regression methods are presented, namely, the linear programming (LP)-based method and the quadratic programming (QP) one. Both methods are illustrated by means of some examples.

Keywords

House Price Linear Programming Problem Interval Regression Fuzzy Data Fuzzy Regression 
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.

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References

  1. 1.
    Celmins, A. (1987a). Least squares model fitting to fuzzy vector data, Fuzzy Sets and Systems 22, 245–269.MathSciNetCrossRefGoogle Scholar
  2. 2.
    Celmins, A. (1987b). Multidimensional fitting of fuzzy model, Mathematical Modeling 9, 669–690.MATHCrossRefGoogle Scholar
  3. 3.
    Diamond, P. (1988). Fuzzy least squares, Inform. Sci. 46, 141–157.MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Dubois, D. and Prade, H. (1988). Possibility Theory. Plenum Press, New York.MATHCrossRefGoogle Scholar
  5. 5.
    Hayashi, I. and Tanaka, H. (1990). The fuzzy GMDH algorithm by possibility models and its application, Fuzzy Sets and Systems 36, 245–258.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ishibuchi, H. and Tanaka, H. (1993). A unified approach to possibility and necessity analysis with interval regression models, Proc. 5th IFSA World Congress, 501–504.Google Scholar
  7. 7.
    Kacprzyk, J. and Fedrizzi, M. (Eds.) (1992), Fuzzy Regression Analysis, Physica-Verlag, Heidelberg.MATHGoogle Scholar
  8. 8.
    Pawlak, Z. (1984). Rough classification, Intern. J. Man-Mach. Stud. 20, 469–485.MATHCrossRefGoogle Scholar
  9. 9.
    Tanaka, H. (1987). Fuzzy data analysis by possibilistic linear models, Fuzzy Sets and Systems 24, 363–375.MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Tanaka, H. and Guo, P. (1999). Possibilistic Data Analysis for Operations Research. Physica-Verlag, Heidelberg.MATHGoogle Scholar
  11. 11.
    Tanaka, H., Hayashi, I. and Watada, J. (1987). Possibilistic linear regression analysis based on possibility measure, Proc. 2nd IFSA World Congress, 317–320.Google Scholar
  12. 12.
    Tanaka, H., Hayashi, I. and Watada, J. (1989). Possibilistic linear regression analysis for fuzzy data, European J. Oper. Res. 40 389–396.MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Tanaka, H. and Ishibuchi, H. (1991). Identification of possibilistic linear system by quadratic membership functions, Fuzzy Sets and Systems 36, 145–160.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Tanaka, H., Ishibuchi, H. and Yoshikawa, S. (1995). Exponential possibility regression analysis, Fuzzy Sets and Systems 69, 305–318.MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Tanaka, H. and Lee, H. (1998). Interval regression analysis by quadratic programming approach, IEEE Trans. Fuzzy Systems 6, 473–481.CrossRefGoogle Scholar
  16. 16.
    Tanaka, H., Lee, H. and Guo, P. (1998). Possibility data analysis with rough set concept, Proc. 6th IEEE Intern. Confer. Fuzzy Syst., 117–122.Google Scholar
  17. 17.
    Tanaka, H., Uejima, S. and Asai, K. (1982). Linear regression analysis with fuzzy model, IEEE Trans. Syst., Man Cyber. 12, 903–907.MATHCrossRefGoogle Scholar
  18. 18.
    Tanaka, H. and Watada, J. (1988). Possibilistic linear systems and their application to the linear regression model, Fuzzy Sets and Systems 27, 275–289.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hideo Tanaka
    • 1
  • Peijun Guo
    • 2
  1. 1.Toyohashi Sozo CollegeUshikawacho, ToyohashiJapan
  2. 2.Faculty of EconomicsKagawa UniversityTakamatsu, KagawaJapan

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