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Heuristic Approches to Fuzzy Regression
S. Mahmoud Taheri
University of Tehran
Abstract:   (75 Views)

There are two main approches to the fuzzy regression (more precisely: regression in fuzzy environment): the least of sum  of distances (including two methods of least squared errors and least absolute errors) and the possibilistic method (the method of least whole vaguness under some restrictions). Beside, some heuristic methods have been proposed to deal with  fuzzy regression. Some of them are based on a combination of two mentioned approaches. Some of them are based on computational algorithmes. A few of heuristic methods use the fuzzy inference systems. Also, there are some methods based on clustering,  artificial neural networks, evolutionary algorithms, and nonparametric procedures.
In this paper, a history and basic ideas of the two main approaches to
fuzzy regression are reveiwed, and some heuristic methods in this topic are investigated. Moreover, 10 criterion are proposed  by which one can

evaluate and compare fuzzy regression models.


Keywords: least of sum of errors, possibilistic regression, heuristic methods, clustering, variable spreads.
Full-Text [PDF 140 kb]   (32 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/09/15 | Accepted: 2018/04/9 | Published: 2018/04/9
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Taheri S M. Heuristic Approches to Fuzzy Regression. Andishe. 2018; 22 (2)
URL: http://andisheyeamari.irstat.ir/article-1-448-en.html

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