Distance Measures for Hesitant Fuzzy Linguistic Sets and Their Applications in Multiple Criteria Decision Making
Distance Measures for Hesitant Fuzzy Linguistic Sets and Their Applications in Multiple Criteria Decision Making
复制标题
犹豫模糊语言集的距离测度及其在多准则决策中的应用
DOI：
10.1007/s40815-018-0460-0
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Dan Peng
中科院分区：
文献类型：
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作者：
Donghai Liu;Dan Peng
犹豫模糊语言术语集（HFLTS）提供了语言和计算基础，以增加基于模糊语言方法的语言启发的灵活性和丰富性。在传统的汉明距离、欧氏距离和广义距离的基础上，提出了一类新的犹豫模糊语言数距离测度，包括犹豫模糊元的犹豫度，并应用了一些语言尺度函数。我们还定义了两个 HFLTS 集合之间的连续距离度量。此外，提出了基于TOPSIS方法的犹豫模糊语言多准则决策距离度量，分别计算备选方案与正理想解、负理想解之间的距离。然后，计算与理想解决方案的相对接近程度，对所有备选方案进行排序。所提出的距离度量的主要特点是它不仅考虑了犹豫模糊元素的犹豫，而且处理了不同语义情况下的语言转换问题，有效地避免了信息丢失和失真。最后通过算例说明了该方法的可行性和有效性，并与现有方法进行了比较。
Hesitant fuzzy linguistic term sets (HFLTSs) provide a linguistic and computational basis to increase the flexibility and richness of linguistic elicitation based on the fuzzy linguistic approach. Based on the traditional Hamming distance, Euclidean distance and generalized distance, some new class of distance measures for hesitant fuzzy linguistic numbers which include the hesitance degree of hesitant fuzzy element are provided and some linguistic scale functions are applied. We also define the continuous distance measure between two collections of HFLTSs. Furthermore, the proposed distance measures based on TOPSIS method for hesitant fuzzy linguistic multiple criteria decision making are developed, which calculate the distances between the alternatives and the positive ideal solution, the negative ideal solution, respectively. Then, the relative closeness degree to the ideal solution is calculated to rank all the alternatives. The main characteristics of the proposed distance measures are that it not only considers the hesitance of the hesitant fuzzy elements but also deals with linguistic transformation problem under different semantic situations, which efficiently avoid information loss and distortion. Finally, an example is provided to illustrate the feasibility and effectiveness of the developed method, which are then compared to the existing methods.
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影响因子：
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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.
影响因子：
7
作者：
Torra, Vicenc
通讯作者：
Torra, Vicenc
DOI：
10.1017/cbo9781139162586.015
发表时间：
2011
期刊：
--
影响因子：
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作者：
D. Hogarth
通讯作者：
D. Hogarth
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：
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作者：
通讯作者：
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