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Published in: Neural Computing and Applications 6/2013

01-05-2013 | Original Article

Designing an algorithm for evaluating decision-making units based on neural weighted function

Author: R. Saneifard

Published in: Neural Computing and Applications | Issue 6/2013

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Abstract

Fuzzy systems have gained more and more attention from researchers and practitioners of various fields. In such systems, the output represented by a fuzzy set sometimes needs to be transformed into a scalar value, and this task is known as the defuzzification process. Several analytic methods have been proposed for this problem, but in this paper, firstly the researchers introduce a novel parametric distance between fuzzy numbers and secondly suggest a new approach to the problem of defuzzification, using this distance. This defuzzification can be used as a crisp approximation with respect to fuzzy quantity. By considering this and with benchmark between fuzzy numbers, we can present a method for evaluating. The method can effectively evaluate various fuzzy numbers and their images and overcome the shortcomings of the previous techniques.

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Metadata
Title
Designing an algorithm for evaluating decision-making units based on neural weighted function
Author
R. Saneifard
Publication date
01-05-2013
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 6/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0878-5

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