FUZZY LINEAR REGRESSION BASED ON LEAST ABSOLUTES DEVIATIONS

被引：0
|
作者：
Taheri, S. M. [1 ,2 ]
Kelkinnama, M. [1 ]
机构：
[1] Isfahan Univ Technol, Dept Math Sci, Esfahan 8415683111, Iran
[2] Ferdowsi Univ Mashhad, Sch Math Sci, Dept Stat, Mashhad, Iran
来源：
IRANIAN JOURNAL OF FUZZY SYSTEMS | 2012年 / 9卷 / 01期
关键词：
Fuzzy regression; Least absolutes deviations; Metric on fuzzy numbers; Similarity measure; Goodness of fit; LOGISTIC-REGRESSION; MODEL; INPUT;
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. A least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. The proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by three goodness of fit criteria. Three well-known data sets including two small data sets as well as a large data set are employed for such comparisons.
引用
收藏
页码：121 / 140
页数：20
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