Some issues on intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm
Some issues on intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm
复制标题
基于阿基米德t-conorm和t-norm的直觉模糊聚合算子的一些问题
DOI:
10.1016/j.knosys.2012.02.004
复制
发表时间:
2012-07
影响因子:
8.8
通讯作者:
Bin Zhu
中科院分区:
文献类型:
--
作者:
Meimei Xia;Zeshui Xu;Bin Zhu
Archimedean t-conorm and t-norm are generalizations of a lot of other t-conorms and t-norms, such as Algebraic, Einstein, Hamacher and Frank t-conorms and t-norms or others, and some of them have been applied to intuitionistic fuzzy set, which contains three functions: the membership function, the non-membership function and the hesitancy function describing uncertainty and fuzziness more objectively. Recently, Beliakov et al. [3] constructed some operations about intuitionistic fuzzy sets based on Archimedean t-conorm and t-norm, from which an aggregation principle is proposed for intuitionistic fuzzy information. In this paper, we propose some other operations on intuitionistic fuzzy sets, study their properties and relationships, and based on which, we study the properties of the aggregation principle proposed by Beliakov et al. [3], and give some specific intuitionistic fuzzy aggregation operators, which can be considered as the extensions of the known ones. In the end, we develop an approach for multi-criteria decision making under intuitionistic fuzzy environment, and illustrate an example to show the behavior of the proposed operators.
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DOI:
10.1142/9789814447393_0015
发表时间:
1995
期刊:
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影响因子:
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作者:
G. Klir;Benjamin J. C. Yuan
通讯作者:
G. Klir;Benjamin J. C. Yuan
影响因子:
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作者:
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ZADEH, LA
DOI:
10.1016/j.knosys.2011.06.004
发表时间:
2011-12
期刊:
Knowl. Based Syst.
影响因子:
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作者:
E. Castiñeira;C. Torres-Blanc;S. Cubillo
通讯作者:
E. Castiñeira;C. Torres-Blanc;S. Cubillo
DOI:
10.1016/j.knosys.2011.05.013
发表时间:
2011-12
期刊:
Knowl. Based Syst.
影响因子:
--
作者:
Shouzhen Zeng;Weihua Su
通讯作者:
Shouzhen Zeng;Weihua Su
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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