Synthetic Correlation Coefficient Between Hesitant Fuzzy Sets with Applications
Synthetic Correlation Coefficient Between Hesitant Fuzzy Sets with Applications
复制标题
犹豫模糊集之间的综合相关系数及其应用
DOI：
10.1007/s40815-018-0496-1
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发表时间：
2018-05
影响因子：
4.3
通讯作者：
Zheng Zhou
中科院分区：
文献类型：
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作者：
Xin Guan;Guidong Sun;Xiao Yi;Zheng Zhou
犹豫模糊集在模糊领域中越来越受到人们的关注。作为高频系统的一个重要研究方向，高频系统之间的相关系数度量一直是研究的热点。虽然在前面的文章中已经提出了一些相关系数，但我们必须声明，现有的相关系数在某些情况下或多或少是违反直觉的。一方面，他们只考虑了HFSS的一个特征，而忽略了其他一些对相关系数有贡献的重要特征。另一方面，它们要求HFS中每个犹豫模糊元素（HFE）的成员长度相同。因此，本文指出了现有相关系数的不足，提出了综合考虑隶属度的完整性、分布性和长度的综合相关系数。首先，我们定义了HFE和HFS的均值、方差和长度率等基本概念来表示HFE和HFS的完整性、分布性和长度。其次，在这些基本概念的基础上，我们定义了均值、方差和长度相关系数。此外，我们通过加权这三个基本相关系数来构造合成相关系数。此外，为了科普实际问题，我们将综合相关系数扩展为加权形式。最后，将综合相关系数应用于数据关联、模式识别、医疗诊断、决策和聚类分析等信息融合问题。通过沿着实例，详细说明了综合相关系数在验证性、判别性、准确性、直观性和效率等方面的优越性。
Hesitant fuzzy sets (HFSs) are becoming more and more popular in the fuzzy domain and attract great attentions. As an important research orientation of HFSs, the correlation coefficient measurement between HFSs is a hot topic. Although some correlation coefficients have been proposed in the previous paper, we have to claim that the existing correlation coefficients are more or less counter-intuitive under some circumstances. On one hand, they consider only one feature of the HFSs and ignore some other important features contributing to the correlation coefficient. On the other hand, they require the lengths of the memberships of each hesitant fuzzy element (HFE) in the HFSs to be same. Therefore, we point out the shortcomings of the existing correlation coefficients in this paper and propose the synthetic correlation coefficient between the HFSs considering the integrality, the distribution and the length of the membership. Firstly, we define such basic concepts as the mean, the variance and the length rate of the HFEs and HFSs to represent the integrality, the distribution and the length. Secondly, based on these basic concepts, we define the mean, the variance and the length correlation coefficients. Furthermore, we construct the synthetic correlation coefficient by weighting these three basic correlation coefficients. In addition, to cope with the practical issues, we extend the synthetic correlation coefficient to the weighted form. Finally, we apply the synthetic correlation coefficient to such information fusion problems as data association, pattern recognition, medical diagnosis, decision making and cluster analysis. Along with some practical examples, the superiority of the proposed synthetic correlation coefficient in validation, discrimination, accuracy, intuitiveness and efficiency is illustrated in detail.
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影响因子：
--
作者：
ZADEH, LA
通讯作者：
ZADEH, LA
影响因子：
7
作者：
Rodriguez, R. M.;Martinez, L.;Herrera, F.
通讯作者：
Herrera, F.
决策中信息融合的犹豫模糊集的位置和视角分析。
影响因子：
18.6
作者：
R.M. Rodríguez;B. Bedregal;H. Bustince;Y.C. Dong;B. Farhadinia;C. Kahraman;L. Martínez;V. Torra;Y.J. Xu;Z.S. Xu;F. Herrera
通讯作者：
F. Herrera
影响因子：
7
作者：
Torra, Vicenc
通讯作者：
Torra, Vicenc
DOI：
10.1049/pbpo161e_ch3
发表时间：
2021-07
期刊：
Artificial Intelligence for Smarter Power Systems: Fuzzy logic and neural networks
影响因子：
--
作者：
通讯作者：
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