多粒度邻域粗糙直觉模糊集模型<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201701002

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Abstract The combination of the multi-granulation neighborhood rough set and the intuitionistic fuzzy set is further researched in this paper. Firstly, the concepts of the intuitionistic fuzzy covering-based rough membership and non-membership are defined for dealing with the heterogeneous data including categorical attributes and numerical attributes. Secondly, a multi-granulation neighborhood rough intuitionistic fuzzy set model is established based on different attribute set sequences and different neighborhood radii. Then, the properties of multi-granulation neighborhood rough intuitionistic fuzzy set are discussed. Next, the approximate sets of the optimistic and pessimistic multi-granulation neighborhood rough intuitionistic fuzzy sets are constructed and their properties are discussed. Finally, these models are illustrated with examples. Example analysis shows the models can handle the heterogeneous data including categorical attributes and numerical attributes more accurately.

Key words： Intuitionistic Fuzzy Set Multi granularity Rough Set Neighborhood Rough Set

Received: 04 August 2016

ZTFLH:

TP 181

About author:: XUE Zhan′ao(Corresponding author), born in 1963, Ph.D., professor. His research interests include basic theory of artificial intelligence and rough set theory.SI Xiaomeng, born in 1989, master stu-dent. Her research interests include intuitionistic fuzzy set theory and rough set theory.YUAN Yilin, born in 1991, master stu-dent. Her research interests include intuitionistic fuzzy set theory and rough set theory.XIN Xianwei, born in 1991, master stu-dent. His research interests include intuitionistic fuzzy set theory, rough set theory and decision theory.

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Cite this article:

XUE Zhan′ao,SI Xiaomeng,YUAN Yilin等. Model of Multi-granulation Neighborhood Rough Intuitionistic Fuzzy Sets[J]. , 2017, 30(1): 11-20.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201701002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I1/11

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