A rough set approach to intuitionistic fuzzy soft set based decision making
A rough set approach to intuitionistic fuzzy soft set based decision making
复制标题
基于直觉模糊软集决策的粗糙集方法
DOI：
10.1016/j.apm.2011.11.071
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发表时间：
2012-10
影响因子：
5
通讯作者：
Zhang, Zhiming
中科院分区：
文献类型：
--
作者：
Zhang, Zhiming
软集合理论最早是由Molodtsov提出的，可以作为处理不确定性的通用数学工具。软集理论自出现以来，在实际应用方面取得了一些进展，特别是在决策中的应用。直觉模糊软集是直觉模糊集和软集的组合。粗糙集理论是处理信息系统中知识的不确定性、颗粒性和不完备性的有力工具。利用粗糙集理论，提出了一种解决直觉模糊软集决策问题的新方法。首先，利用直觉模糊关系和阈值对，定义了一种新的粗糙集模型，并研究了该模型的一些基本性质。然后给出了近似精度和粗糙度的概念，并讨论了它们的一些基本性质。此外，我们还研究了直觉模糊软集与直觉模糊关系之间的关系，提出了一种基于直觉模糊软集的粗糙集决策方法。最后，通过一个实例说明了该粗糙集方法在基于直觉模糊软集的决策问题中的有效性。
The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. Since its appearance, there has been some progress concerning practical applications of soft set theory, especially the use of soft sets in decision making. The intuitionistic fuzzy soft set is a combination of an intuitionistic fuzzy set and a soft set. The rough set theory is a powerful tool for dealing with uncertainty, granuality and incompleteness of knowledge in information systems. Using rough set theory, this paper proposes a novel approach to intuitionistic fuzzy soft set based decision making problems. Firstly, by employing an intuitionistic fuzzy relation and a threshold value pair, we define a new rough set model and examine some fundamental properties of this rough set model. Then the concepts of approximate precision and rough degree are given and some basic properties are discussed. Furthermore, we investigate the relationship between intuitionistic fuzzy soft sets and intuitionistic fuzzy relations and present a rough set approach to intuitionistic fuzzy soft set based decision making. Finally, an illustrative example is employed to show the validity of this rough set approach in intuitionistic fuzzy soft set based decision making problems.
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DOI：
10.1007/978-3-319-99368-3
发表时间：
2018-10
期刊：
--
影响因子：
--
作者：
R. Efendi;Voni Apriana Dewi;Rahmadeni;Sri Basriati;Dadang Syarif
通讯作者：
R. Efendi;Voni Apriana Dewi;Rahmadeni;Sri Basriati;Dadang Syarif
影响因子：
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作者：
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通讯作者：
ZADEH, LA
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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DOI：
10.1016/j.camwa.2008.07.013
发表时间：
2008-12
期刊：
Comput. Math. Appl.
影响因子：
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作者：
Z. Kong;Liqun Gao;Lifu Wang;Steven Li
通讯作者：
Z. Kong;Liqun Gao;Lifu Wang;Steven Li
DOI：
10.1016/j.mcm.2010.11.080
发表时间：
2011-03
期刊：
Math. Comput. Model.
影响因子：
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作者：
Yuncheng Jiang;Hai Liu;Yong Tang;Qimai Chen
通讯作者：
Yuncheng Jiang;Hai Liu;Yong Tang;Qimai Chen