A new transformation algorithm for multi-granularity unbalanced linguistic terms

被引：0

作者：

Wang, Xianqin ^{[1
]}

Zhou, Bin

Yi, Liangzhong ^{[2
]}

Li, Xiaohui ^{[1
]}

机构：

[1] Xihua Univ, Coll Sci, Chengdu 610039, Sichuan, Peoples R China

[2] Sichuan Police Coll, Luzhou 646000, Sichuan, Peoples R China

来源：

2017 IEEE 2ND ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC) | 2017年

关键词：

Multi-granularity; Unbalanced linguistic terms; 2-tuple linguistic model; Group decision making; GROUP DECISION-MAKING; PREFERENCE RELATIONS; AGGREGATION OPERATORS; MODEL; CONTEXTS; FUSION; SETS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The aim of this paper is to propose a transformation algorithm for multi-granularity linguistic information assessed in different unbalanced linguistic term sets together with its application in linguistic group decision making (LGDM) problem. Assuming that the linguistic information given to the alternatives by different decision makers distribute in different granularity and/or semantic term sets. First, the transformation functions for linguistic preference information based on normalized accumulative area of triangle membership function and 2-tuple linguistic model are present to make linguistic information uniform, and then the linguistic transformation algorithm is proposed. Finally, an application is introduced to illustrate the feasibility and effectiveness of the algorithm.

引用

页码：1640 / 1644

页数：5

共 16 条

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