Fuzzy Linear Regression Model Using the Least Hausdorf-distance Square Method

  • Choi, Sang-Sun (Department of Statistics, Kyungpook National University) ;
  • Hong, Dug-Hun (School of Mechanical and Automotive Engineering, Catholic University) ;
  • Kim, Dal-Ho (Department of Statistics, Kyungpook National University)
  • Published : 2000.12.01

Abstract

In this paper, we review some class of t-norms on which fuzzy arithmetic operations preserve the shapes of fuzzy numbers and the Hausdorff-distance between fuzzy numbers as the measure of distance between fuzzy numbers. And we suggest the least Hausdorff-distance square method for fuzzy linear regression model using shape preserving fuzzy arithmetic operations.

Keywords

References

  1. Fuzzy Sets and System v.37 Note on Fuzzy regression Bardossy, A.
  2. Information Science v.46 Fuzzy least squares Diamond, P.
  3. Fuzzy Sets and Systems : Theory and Applications Dubois, D.;Prade, H.
  4. Fuzzy Sets and Systems v.101 Using Yager's t-norms for aggregationof fuzzy intervals Hauke, W.
  5. Fuzzy Sets and Systems v.90 Fuzzy system reliability analysis by the use of T_w(the weakest t-norm) on fuzzy number arithmetic operations Hong, D.H.;Do, H.Y.
  6. European Journal of operational research v.92 Theory and Methodology Fuzzy versus statistical linear regression Kim, K.J.;Moskowitz, H.;Koksalan, M.
  7. statistics v.21 no.4 Linear Regression with Random Fuzzy Observations Nather, W.;Albrecht, M.
  8. Fuzzy Regression Analysis Approximate maximum likelihood estimates in regression analysis for fuzzy obsevation data Okuda, T.;Kodono, K.;Asai, K.;Kacprzyk, J.(ed.);Fedrizzi, E.(ed.)
  9. Fuzzy Sets and Systems v.47 Multiobjective fuzzy linear regression analysis for fuzzy input-output data Sakswa, M.;Yano, H.
  10. Advances in Fuzzy Set Theory and Applications Fuzzy information and decision in statistical model Tanaka, H.;Okuda, T.;Asai, K.;M.M. Gupta(ed.);R.K. Ragade(ed.);R.R. Yager
  11. International Congress on Applied Systems Research and Cybernetics Fuzzy linear regression model Tanaka, H.;Uejima, S.;Asia, K.
  12. IEEE Trans. Man. Cybernet v.12 no.6 Linear regression analysis with fuzzy model Tanaka, H.;Uejima, S;Asai, K.
  13. FIP-84 Conference Fuzzy linear regression analysis of the number of state in local government Tanaka, H.;Shimomura, T.;Asai, K.
  14. Statistical Method for Non-Precise DATA Viertl, R.
  15. Cybernetics and Systems Research v.2 M-fuzzy numbers and random variables Wang, Z.;R. Trappl(ed.)
  16. Fuzzy Systems and Knowledge Engineering Fuzzy linear regression of fuzzy valued variables Wang, Z.
  17. Fuzzy Sets and Systems v.36 Fuzzy linear regression analysis of fuzzy valued variables Wang, Z.;Li, S.
  18. Fuzzy Sets and Systems v.1 Fuzzy sets as basis for a theory of possibility Zadeh, L.A.