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Enhancing multiple attribute group decision making flexibility based on information fusion technique and hesitant Pythagorean fuzzy sets

机译:基于信息融合技术和犹豫不决的勾股模糊集的多属性群决策灵活性

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The aim of this paper is to develop some novel hesitant Pythagorean fuzzy sets (HPFSs) based methods in enhancing fuzzy related problems flexibility. In view of the effectiveness of hesitant fuzzy sets (HFSs) for expressing the hesitant situation, and the powerfulness of Pythagorean fuzzy sets (PFSs) in handling vagueness and uncertainty, we combine the best aspects of HFSs and PFSs and consider a new concept called the HPFSs. Firstly, some existing basic properties and operators are extended and improved in detail. For the sake of application, considering the deficiencies of pessimistic-optimistic normalization principles in dealing with hesitant fuzzy related sets, on the basis of lowest common multiple principle in number theory, an improved normalization algorithm is proposed to reserve the fidelity of original information. Then, a flexible and multipurpose generalized distance measure for HPFSs is presented. Further, under the HPFSs environment, a multiple attribute group decision making method based on the extended hesitant Pythagorean fuzzy VIKOR is presented. Three numerical examples, one from the bidirectional approximate reasoning system and other from the medical diagnosis and selection model of health management center, are presented to elaborate on the performance of our approach and compared their results with the several existing approaches results.
机译:本文的目的是开发一些新颖的基于犹豫的勾股勾股模糊集(HPFSs)的方法,以增强模糊相关问题的灵活性。鉴于犹豫模糊集(HFS)表示犹豫情况的有效性以及勾股勾股模糊集(PFS)在处理模糊性和不确定性方面的强大功能,我们结合了HFS和PFS的最佳方面,并考虑了一个称为HPFS。首先,对现有的一些基本属性和运算符进行了详细的扩展和改进。为了便于应用,考虑到悲观乐观归一化原理在处理犹豫模糊相关集时的不足,在数论中的最低公倍数原理的基础上,提出了一种改进的归一化算法来保留原始信息的保真度。然后,提出了一种针对HPFS的灵活多用途广义距离度量。进一步,在HPFSs环境下,提出了一种基于扩展犹豫的勾股勾股模糊VIKOR的多属性群决策方法。给出了三个数值示例,一个来自双向近似推理系统,另一个来自健康管理中心的医疗诊断和选择模型,以详细说明我们的方法的性能并将其结果与几种现有方法的结果进行比较。

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