区间直觉正态模糊数集成算子及其在决策中的应用

Interval-valued Intuitionistic Normal Fuzzy Aggregation Operators and Their Application to Decision Making

研究了区间直觉正态模糊数(IVINFN)决策信息及其集成算子。首先,定义了区间直觉正态模糊数的概念,提出了运算法则;其次,给出了区间直觉正态模糊数诱导有序加权平均(IVINFN-IOWA)算子和区间直觉正态模糊数诱导有序加权几何(IVINFN-IOWGA)算子的概念,探讨了其性质;在此基础上,分别定义了基于均值和标准差的区间直觉正态模糊数的得分函数和精确函数,给出其排序方法。最后,针对属性值为区间直觉正态模糊数且权重已知的多属性决策问题,给出了其决策方法,并进行了实例分析,结果表明该决策方法是有效的。

Interval-valued intuitionistic normal fuzzy number and its aggregation operators are investigated in this paper. Firstly, interval-valued intuitionistic normal fuzzy number （IVINFN） is defined and its operational laws are proposed. Secondly, IVINFN induced ordered weighted averaging （IVINFN-IOWA） operator and IVINFN induced ordered weighted geometric averaging （IVINFN-IOWGA） are presented. Their pr, operties are discussed. Based on mean and standard deviation, the score function and accurate function of IVINFN are defined, which are used for ranking IVINFNs, Finally, an approach is given to solve the multi-attribute decision making problem in which the information is provided by IVINFNs, and an example is illustrated to show that the proposed method is effective and feasible.