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基于联系数广义有序加权平均算子的三角模糊数多属性决策方法

A method for multiple attribute decision making based on connection number generalized ordered weighted average operator
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摘要 为解决属性值和属性权重都是三角模糊数时的多属性决策问题,采用等价转换的思路将属性值和属性权重的三角模糊数转换成同异型二元联系数,进而将三角模糊数的多属性决策转换成二元联系数的多属性决策.基于对二元联系数的有效信息集成,构建了一种新型的联系数广义有序加权平均(CNGOWA)算子,并提出了一种基于CNGOWA算子的三角模糊数多属性决策方法.实例分析表明,基于CNGOWA算子的三角模糊数多属性决策方法合理、稳定,可应用于三角模糊数型的模糊数据的决策分析和应用. In order to solve the multiple attribute decision making problem when attribute values and attribute weights are triangular fuzzy numbers,the idea of equivalent transformation is adopted to convert the triangular fuzzy numbers of attribute values and attribute weights into the same and different binary connection numbers,and then the multiple attribute decision making of triangular fuzzy numbers is converted into the multiple attribute decision making of binary connection numbers.Based on effective information integration of binary connection numbers,a new generalized ordered weighted average(CNGOWA)operator of connection numbers is constructed,and a triangular fuzzy number multiple attribute decision making method based on CNGOWA operator is proposed.An example analysis shows that the method is reasonable and stable,and can be applied to the analysis and application of triangular fuzzy number type fuzzy data decision making.
作者 袁宏俊 卢敏欣 江立辉 YUAN Hongjun;LU Minxin;JIANG Lihui(School of Statistics and Applied Mathematics,Anhui University of Finance and Economics,Bengbu 233030,China;School of Statistics,Dongbei University of Finance and Economics,Dalian 116025,China;School of Finance,Anhui University of Finance and Economics,Bengbu 233030,China;School of Artificial Intelligence and Big Data,Hefei University,Hefei 230601,China)
出处 《延边大学学报(自然科学版)》 CAS 2020年第1期8-14,共7页 Journal of Yanbian University(Natural Science Edition)
基金 安徽省教育厅人文社会科学重点研究项目(SK2020A0028,SK2018A0605) 安徽财经大学重点科研基金资助项目(ACKY1713ZDB) 合肥学院科研基金资助项目(17ZR06ZDA)。
关键词 多属性决策 三角模糊数 联系数广义有序加权平均算子 二元联系数 均值方差 multiple attribute decision making triangular fuzzy number CNGOWA operator binary connection number mean-variance
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