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

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An Erratum to this article was published on 28 August 2012

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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Authors and Affiliations

  1. Department of Applied Mathematics, Urmia Branch, Islamic Azad University, Oroumieh, Iran

    R. Saneifard

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  1. R. Saneifard
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Correspondence to R. Saneifard.

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Saneifard, R. Designing an algorithm for evaluating decision-making units based on neural weighted function. Neural Comput & Applic 22, 1125–1131 (2013). https://doi.org/10.1007/s00521-012-0878-5

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