Some new Pythagorean fuzzy correlation techniques via statistical viewpoint with applications to decision-making problems
Some new Pythagorean fuzzy correlation techniques via statistical viewpoint with applications to decision-making problems
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DOI：
10.3233/jifs-202469
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发表时间：
2021-01-01
影响因子：
2
通讯作者：
Chen, Jia
中科院分区：
文献类型：
--
作者：
Ejegwa, Paul Augustine;Wen, Shiping;Chen, Jia
与直觉模糊集相比，勾股模糊集具有抑制不确定数据的能力，是一种可靠的软计算技术。在Pythagorean模糊环境中的几种度量工具中，相关系数是非常重要的，因为它能够度量任意两个Pythagorean模糊集（PFS）之间的相互依赖性和相互关系。在毕达哥拉斯模糊相关系数中，已有一些从统计学角度计算模糊函数系统相关系数的方法，但这些方法存在一定的局限性：（1）不能考虑模糊函数系统的所有参数，导致信息丢失;（2）计算结果不精确;（3）性能指标较少。其次，本文介绍了一些新的统计技术，计算CCPFS的勾股模糊方差和协方差，解决了局限性，更好的性能指标。新技术结合了PFS的三个参数，并定义在范围[-1，1]内，以显示PFS之间的相关性的功率，并指示所考虑的PFS是负相关还是正相关。计算CCPFS的新的统计技术的有效性进行了测试，通过考虑一些数值例子，其中新的技术表现出上级性能指标相比，类似的现有的。为了证明新的统计技术计算CCPFS的适用性，一些多准则决策问题（MCDM），涉及医疗诊断和模式识别问题，确定通过新的技术。
Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques.