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
Link To Document