A new ranking method for Pythagorean fuzzy numbers

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2017-11-01 DOI:10.1109/ISKE.2017.8258763

S. Wan, Zhen Jin, Feng Wang

{"title":"A new ranking method for Pythagorean fuzzy numbers","authors":"S. Wan, Zhen Jin, Feng Wang","doi":"10.1109/ISKE.2017.8258763","DOIUrl":null,"url":null,"abstract":"Pythagorean fuzzy set (PFS), as an extension of intuitionistic fuzzy set, has received great attention in decision field. How to rank Pythagorean fuzzy numbers (PFNs) is a critical issue during the decision process. Thus, this paper focuses on the ranking method for PFNs. The main works are outlined as follows: (1) Existing ranking methods for PFNs are reviewed. Some examples are proposed to illustrate their limitations. (2) To overcome these limitations, the concepts of knowledge measure and information reliability of PFN are presented to describe the amount and quality of information of PFNs. It is comprehensive to involve the information of positive ideal point, negative ideal point and fuzzy point. (3) Motivated by the concept of relative closeness degree, an arc-length based relative closeness degree of PFN is proposed and interpreted geometrically. Moreover, the arc-length based relative closeness degree is simple and convenient for calculation. (4) A ranking method for PFNs is put forward on the basis of knowledge measure, information reliability and an arc-length based relative closeness degree.","PeriodicalId":208009,"journal":{"name":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISKE.2017.8258763","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 6

Abstract

Pythagorean fuzzy set (PFS), as an extension of intuitionistic fuzzy set, has received great attention in decision field. How to rank Pythagorean fuzzy numbers (PFNs) is a critical issue during the decision process. Thus, this paper focuses on the ranking method for PFNs. The main works are outlined as follows: (1) Existing ranking methods for PFNs are reviewed. Some examples are proposed to illustrate their limitations. (2) To overcome these limitations, the concepts of knowledge measure and information reliability of PFN are presented to describe the amount and quality of information of PFNs. It is comprehensive to involve the information of positive ideal point, negative ideal point and fuzzy point. (3) Motivated by the concept of relative closeness degree, an arc-length based relative closeness degree of PFN is proposed and interpreted geometrically. Moreover, the arc-length based relative closeness degree is simple and convenient for calculation. (4) A ranking method for PFNs is put forward on the basis of knowledge measure, information reliability and an arc-length based relative closeness degree.

查看原文本刊更多论文

毕达哥拉斯模糊数的一种新的排序方法

毕达哥拉斯模糊集作为直觉模糊集的扩展，在决策领域受到了广泛的关注。如何对毕达哥拉斯模糊数进行排序是决策过程中的关键问题。因此，本文主要研究pfn的排序方法。主要工作如下:(1)对现有的pfn排序方法进行了综述。提出了一些例子来说明它们的局限性。(2)为了克服这些局限性，提出了PFN的知识测度和信息可靠性的概念来描述PFN信息的数量和质量。它综合了正理想点、负理想点和模糊点的信息。(3)基于相对贴近度的概念，提出了基于弧长的PFN相对贴近度，并进行了几何解释。此外，基于弧长的相对贴近度计算简单方便。(4)提出了一种基于知识测度、信息可靠性和基于弧长相对贴近度的pfn排序方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

自引率

0.00%

发文量