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| 多粒度邻域粗糙直觉模糊集模型* | |
| 引用本文： | 薛占熬,司小朦,袁艺林,辛现伟. 多粒度邻域粗糙直觉模糊集模型*[J]. 模式识别与人工智能, 2017, 30(1): 11-20. DOI: 10.16451/j.cnki.issn1003-6059.201701002 |
| 作者姓名： | 薛占熬 司小朦 袁艺林 辛现伟 |
| 作者单位： | 河南师范大学 计算机与信息工程学院 新乡 453007 河南师范大学 “智慧商务与物联网技术”河南省工程实验室 新乡 453007 |
| 基金项目： | 国家自然科学基金项目(No.61273018)、河南省基础与前沿技术研究计划项目(No.132300410174)、河南省教育厅计划项目(No.14A520082)、新乡市重点科技攻关计划项目(No.ZG14020)资助 |
| 摘 要： | 针对名义型属性和数值型属性并存的混合型数据,结合多粒度邻域粗糙集和直觉模糊集,分别定义模糊覆盖粗糙隶属度和非隶属度.基于不同的属性集序列和不同的邻域半径,构建多粒度邻域粗糙直觉模糊集模型,证明模型相关性质.然后提出乐观和悲观多粒度邻域粗糙直觉模糊集的近似集,并讨论模型性质.最后使用文中模型计算实例,说明其能较好地解决名义型属性和数值型属性的混合型数据的处理问题. |
| 关 键 词： | 直觉模糊集 多粒度粗糙集 邻域粗糙集 |
| 收稿时间： | 2016-08-04 |
Model of Multi-granulation Neighborhood Rough Intuitionistic Fuzzy Sets |
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| XUE Zhan′ao,SI Xiaomeng,YUAN Yilin,XIN Xianwei. Model of Multi-granulation Neighborhood Rough Intuitionistic Fuzzy Sets[J]. Pattern Recognition and Artificial Intelligence, 2017, 30(1): 11-20. DOI: 10.16451/j.cnki.issn1003-6059.201701002 | |
| Authors: | XUE Zhan′ao SI Xiaomeng YUAN Yilin XIN Xianwei |
| Affiliation: | College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007 Henan Engineering Laboratory of Intelligence Business and Internet of Things, Henan Normal University, Xinxiang 453007 |
| 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. |
| Keywords: | Intuitionistic Fuzzy Set Multi granularity Rough Set Neighborhood Rough Set |
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