多粒化模糊软粗糙集模型

计算机工程与应用 ›› 2016, Vol. 52 ›› Issue (21): 49-56.

多粒化模糊软粗糙集模型

柳彦军1，张晓霞2，吴红萍3

1.重庆第二师范学院数学与信息工程系，重庆 400067
2.华北电力大学控制与计算机工程学院，北京 102206
3.西北师范大学数学与统计学院，兰州 730070

出版日期:2016-11-01 发布日期:2016-11-17

Multi-granulation fuzzy soft rough set model

LIU Yanjun1, ZHANG Xiaoxia2, WU Hongping3

1.Department of Mathematics and Information Engineering, Chongqing University of Education, Chongqing 400067, China
2.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
3.School of Mathematics and Statistics, Northwest Normal University, Lanzhou 730070, China

Online:2016-11-01 Published:2016-11-17

摘要/Abstract

摘要： 为了扩大粗糙集理论的应用，特别是在模糊环境中的应用，基于模糊软集和模糊蕴涵算子，主要研究基于软模糊近似空间的乐观多粒化模糊软粗糙集模型。该模型将参数集根据客户的不同要求或目标进行重组，只选择若干相关参数集参与计算上、下近似，这样定义的上、下近似不再由整个属性集决定，而是根据重组后的多个属性集一并生成，从而使结果更加符合实际需求。另外，还定义了乐观多粒化模糊软粗糙集模型的截集并讨论了其相关性质。最后给出了算例。

关键词: 模糊集;粗糙集;模糊软集;多粒化粗糙集;（I, J）-乐观多粒化模糊软粗糙集

Abstract: To enlarge the usage scope of classical rough set theory, particularly in the application of the fuzzy environment, this paper defines the optimistic multi-granulation fuzzy soft rough set model based on fuzzy soft sets and fuzzy implication operator, where a pair of lower and upper optimistic multi-granulation approximations of a fuzzy set are proposed. In fact, this model is constructed based on the recombination of parameter set according to the customers’ particular requirements for parameters. Thus, only parts of parameters are selected to compute the lower and upper optimistic multi-granulation approximations. In addition, it introduces the cut of optimistic multi-granulation rough set and investigates some properties of it. Finally, an example is given to illustrate this model.

Key words: fuzzy sets, rough sets, fuzzy soft sets, multi-granulation rough sets, （I, J）-optimistic multigranulation fuzzy soft rough sets

柳彦军1，张晓霞2，吴红萍3. 多粒化模糊软粗糙集模型[J]. 计算机工程与应用, 2016, 52(21): 49-56.

LIU Yanjun1, ZHANG Xiaoxia2, WU Hongping3. Multi-granulation fuzzy soft rough set model[J]. Computer Engineering and Applications, 2016, 52(21): 49-56.