区间值信息系统中基于邻域系统的粗糙集模型

Pawlak经典粗糙集理论主要针对离散值,对复杂现实世界中的区间值却不能有效支持。在区间值信息系统中,基于灰格运算和Hausdorff距离提出了一种区间值的邻域关系。在该邻域关系基础之上,依次提出了基于邻域关系、最大相容类和邻域系统三种灰色粗集模型,提高了近似空间的精确度;同时讨论了三种灰色粗集模型之间的上、下近似空间,并用实例进行分析及验证。

Neighborhood systems based rough sets in interval-valuedinformation system

Pawlak's rough set theory offers a formal theoretical framework to deal with categorical data, but this model is not applicable to interval data which widely exist in real world applications. By using grey lattice operation and Hausdorff distance, this paper provided a new neighborhood relationship in an interval-valued information system. Furthermore, it proposed three forms of rough set models based on neighborhood relationship, maximal consistent blocks and neighborhood system, which was to improve the accuracy of approximations. Moreover, it employed three numerical examples to substantiate the conceptual arguments.