论文信息 - Further examination of fuzzy linear regression

Further examination of fuzzy linear regression

Abstract Fuzzy linear regression was originally introduced by Tanaka et al. (1982) Sakawa and Yano (1992) expanded fuzzy linear regression by introducing a method that allows fuzzy independent variables as well as a fuzzy dependent variable. The purpose of this paper is to examine the strengths and weaknesses of this formulation, suggest possible improvements, and discuss fundamental questions about fuzzy linear regression.

William H. Woodall | David T. Redden | W. Woodall | D. Redden

[1] Frederick S. Hillier,et al. Introduction of Operations Research , 1967 .

[2] Masatoshi Sakawa,et al. Multiobjective fuzzy linear regression analysis for fuzzy input-output data , 1992 .

[3] H. Ishibuchi,et al. Identification of possibilistic linear systems by quadratic membership functions of fuzzy parameters , 1990 .

[4] John Van Ness,et al. A routine for converting regression algorithms into corresponding orthogonal regression algorithms , 1988, TOMS.

[5] G. Casella,et al. Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[6] Witold Pedrycz,et al. Evaluation of fuzzy linear regression models , 1991 .

[7] Hisao Ishibuchi,et al. Exponential possibility regression analysis , 1995 .

[8] H. Tanka. Fuzzy data analysis by possibilistic linear models , 1987 .

[9] Junzo Watada,et al. Possibilistic linear regression analysis for fuzzy data , 1989 .

[10] William H. Woodall,et al. Properties of certain fuzzy linear regression methods , 1994 .

[11] Georg Peters. Fuzzy linear regression with fuzzy intervals , 1994 .

[12] A. Celmins. Least squares model fitting to fuzzy vector data , 1987 .

[13] Sándor József. On the effect of linear data transformations in possibilistic fuzzy linear regression , 1992 .