Fuzzy robust regression analysis

Author

Watada, Junzo ; Yabuuchi, Yoshiyuki

Author_Institution

Dept. of Ind. Manage., Osaka Inst. of Technol., Japan

fYear

1994

fDate

26-29 Jun 1994

Firstpage

1370

Abstract

Since a fuzzy linear regression model was proposed in 1987, its possibilistic model was employed to analyze data. From viewpoints of fuzzy linear regression, data are understood to express the possibilities of a latent system. When data have error or data are very irregular, the obtained regression model has an unnaturally wide possibility range. We propose a fuzzy robust linear regression which is not influenced by data with error. The model is built as rigid a model as possible to minimize the total error between the model and the data. The robustness of the proposed model is shown using numerical examples

Keywords

fuzzy systems; genetic algorithms; integer programming; possibility theory; statistical analysis; data with error; distance concept; fuzzy linear regression model; fuzzy robust linear regression; genetic algorithm; mixed integer programming problem; possibilistic model; robust regression analysis; robustness; Data analysis; Equations; Fuzzy systems; Genetic algorithms; Linear programming; Linear regression; Regression analysis; Robustness; Technology management; Time series 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.1994.343612

Filename

343612