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:: Volume 22, Issue 2 (3-2018) ::
Andishe 2018, 22(2): 43-52 Back to browse issues page
Heuristic Approches to Fuzzy Regression
S. Mahmoud Taheri
University of Tehran
Abstract:   (448 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 180 kb]   (274 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) :43-52
URL: http://andisheyeamari.irstat.ir/article-1-448-en.html


Volume 22, Issue 2 (3-2018) Back to browse issues page
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