勾股模糊H平均算法及其云计算系统选择

Pythagorean Fuzzy H Mean Algorithms and Their Applications in Cloud Computing System Selection

研究勾股模糊数信息环境下内在关联度的多属性选择策略问题。首先,定义了实现模糊集模并模或功能的勾股模糊数运算;其次,将模糊H平均用于设计聚合算子,提出了勾股模糊H平均(PFHM)算法,并讨论了勾股模糊H平均算法的3个特征性质及应用实例;然后,构建了新的勾股模糊多属性决策模型,该模型将实际问题属性评价值信息转化为勾股模糊数决策矩阵,通过PFHM信息集成算法进行计算,以得分度和精确度量化数据关联性,有效扩展了决策的应用范围;最后,将构建的模型应用于云计算系统选择,通过案例验证了其有效性。

This paper studies the multi-attribute selection strategy of intrinsic correlation degree in the information environment of Pythagorean fuzzy number.Firstly,the Pythagorean fuzzy number operations based on t-norm and t-conorm are defined.Secondly,the fuzzy H mean is integrated into the construction process of aggregation operator,the Pythagorean fuzzy H mean(PFHM)algorithm is proposed,and three properties and several special cases for PFHM are investigated.Then,a new Pythagorean fuzzy multi-attribute decision-making model is constructed,which transforms the attribute evaluation value information of practical problems into Pythagorean fuzzy number decision-making matrix,and calculates it by PFHM information integration algorithm,so as to quantify the data relevance with scores and accuracy,and effectively expand the application scope of decision-making.Finally,the constructed model is applied to the selection of cloud computing systems,and its effectiveness is verified through cases.