摘要
多粒度粗糙集和决策论粗糙集是Pawlak粗糙集的重要推广,目前已成为人工智能研究的热点.然而,它们大多处理的都是单值信息系统中的问题.而实际生活中绝大多数都是处理多值问题,为了解决这一问题,在多集值信息表中将多粒粗糙集与模糊决策论粗糙集相结合进行研究,提出了其在乐观,悲观情形下的上下近似,研究了一些相关性质并给出了多集值信息表中的多粒度模糊决策论粗糙集精度、粗度的概念,最后通过一个具体例子验证其有效性.
Multi-granularity rough sets and decision-theoretic rough sets are important generalizations of Pawlak rough sets,and have become the hotspots of artificial intelligence research in recent years.However,most of them deal with problems in single-valued information systems.In real life,most of them deal with multi-value problems.In order to solve this problem,this paper combines Multi-granular rough sets with fuzzy decision theory rough sets in multi-set-valued information tables,proposes its upper and lower approximation under optimistic and pessimistic conditions,studies some related properties and gives the concepts of precision and roughness of Multi-granular fuzzy decision theory rough sets in multi-set-valued information tables.Then,the algorithms for calculating upper and lower approximations are given.Finally,an example is given to verify the effectiveness of the algorithms.
作者
王虹
柴晓华
WANG Hong;CHAI Xiao-hua(College of Mathematics and Computer Science,Shan’xi Normal University,Lifen 041004,China)
出处
《数学的实践与认识》
北大核心
2020年第8期141-148,共8页
Mathematics in Practice and Theory
基金
省自然科学基金(201601D011043)。
关键词
多集值信息表
模糊决策论粗糙集
多粒度粗糙集
上下近似
multi-set-valued information table
fuzzy decision theory rough set
multi-granularity rough set
upper and lower approximation
