优势关系下直觉模糊信息系统的变精度与程度“逻辑或”粗糙集
The “Logical Or” Rough Set Theory of Variable Precision and Grade Based on Dominance Relation in Intuitionistic Fuzzy Information System
摘要: 本文在直觉模糊信息系统中定义了加权得分函数，然后在此基础上定义了一种新的排序规则，基于此排序规则构造出优势关系。然后引入基于此优势关系的变精度与程度“逻辑或”上下近似的定义，并研究其粗糙集区域的基本结构和重要性质，并设计了相应的算法。最后，引入实际案例验证了该理论的可行性和有效性，进一步为直觉模糊序信息系统的知识发现提供了理论基础。
Abstract: The weighted score function is proposed in the intuitionistic fuzzy information system, and a new sort rule is defined on the basis. Dominance relation of the system is constructed based on the rule. Then the upper and lower approximation of variable precision and degree “logic or” based on the dominance relation is introduced. Moreover, the basic structure and important properties of the rough set region are studied and the corresponding algorithm is designed. Finally, a practical case is introduced to verify the feasibility and effectiveness of the theory, which provides a theoretical basis for the knowledge discovery of intuitionistic fuzzy order information systems.
文章引用：胡猛, 郭艳婷, 徐伟华. 优势关系下直觉模糊信息系统的变精度与程度“逻辑或”粗糙集[J]. 运筹与模糊学, 2016, 6(2): 66-77. https://dx.doi.org/10.12677/ORF.2016.62009

参考文献

[1] Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X [Google Scholar] [CrossRef] 
[2] Atanassov, K. (1986) Fuzzy Sets. Fuzzy Sets and Sys-tems, 20, 87-96. http://dx.doi.org/10.1016/S0165-0114(86)80034-3 [Google Scholar] [CrossRef] 
[3] 徐泽水. 直觉模糊信息集成理论及应用[M]. 北京: 科学出版社, 2008.
[4] 李梁, 张建刚. 基于粗糙集与关联规则的教师科研能力评价[J]. 重庆理工大学学报: 自然科学版, 2014, 28(1): 69-74.
[5] 陈媛, 苟光磊, 卢玲. 基于一致性准则的属性约简改进算法[J]. 重庆理工大学学报: 自然科学版, 2014, 28(5): 79-83.
[6] Pawlak, Z. (1982) Rough Sets. International Journal of Computer and Information Sciences, 11, 341-356. http://dx.doi.org/10.1007/BF01001956 [Google Scholar] [CrossRef] 
[7] Dembczyński, K.R., Pindur, R. and Susmaga, R. (2003) Domin-ance-Based Rough Set Classifier without Induction of Decision Rules. Electronic Notes Theory Computer Science, 82, 84-95. http://dx.doi.org/10.1016/S1571-0661(04)80708-4 [Google Scholar] [CrossRef] 
[8] Dembczyński, K.R., Pindur, R. and Susmaga, R. (2003) Generation of Exhaustive Set of Rules within Dominance- Based Rough Set Approach. Electronic Notes Theory Computer Science, 82, 96-107. http://dx.doi.org/10.1016/S1571-0661(04)80709-6 [Google Scholar] [CrossRef] 
[9] Greco, S., Matarazzo, B. and Slowinski, R. (2002) Rough Approximation by Dominance Relations. International Journal of Intelligence Systems, 17, 153-171. http://dx.doi.org/10.1002/int.10014 [Google Scholar] [CrossRef] 
[10] Yang, X.B., Yang, J.Y., Wu, C., et al. (2008) Dominance-Based Rough Set Approach and Knowledge Reductions in Incomplete Ordered Information Systems. Information Sciences, 178, 1219-1234. http://dx.doi.org/10.1016/j.ins.2007.09.019 [Google Scholar] [CrossRef] 
[11] Qian, Y.H., Dang, C.Y., Liang, J.Y., et al. (2009) Set-Valued Ordered Information Systems. Information Science, 179, 2809-2832. http://dx.doi.org/10.1016/j.ins.2009.04.007 [Google Scholar] [CrossRef] 
[12] Ziarko, W. (1993) Variable Precision Rough Set Model. Journal of Computer System Science, 46, 39-59. http://dx.doi.org/10.1016/0022-0000(93)90048-2 [Google Scholar] [CrossRef] 
[13] Yao, Y.Y. and Lin, T.Y. (1996) Generalization of Rough Sets Using Modal Logics. Intelligent Automation and Soft Computing, 2, 103-119. http://dx.doi.org/10.1080/10798587.1996.10750660 [Google Scholar] [CrossRef] 
[14] 张贤勇, 莫志文. 变精度粗糙集[J]. 模式识别与人工智能, 2004, 17(2): 151-155.
[15] Xu, W., Liu, S. and Wang, Q. (2010) The First Type of Grade Rough Set Based on Rough Membership Function. Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 4, 1922-1926.
[16] Yao, Y.Y. and Lin, T.Y. (1997) Graded Rough Set Approximations Based on Nested Neighborhood Systems. Proceedings of 5th European Congress on Intelligent Techniques and Soft Computing, 1, 196-200.
[17] 徐伟华. 序信息系统与粗糙集[M]. 北京: 科学出版社, 2013.

为你推荐