模糊T划分下基于贴近度的变精度粗糙模糊集模型

Nearness Based variable precision rough fuzzy set model in fuzzy T Partition

粗糙集模型的扩展是粗糙集研究的重要内容之一,根据论域中知识和被近似对象的特点,学者们提出了针对不同情况的粗糙集模型,如Pawlaw针对知识和被近似对象都是确定的,提出了经典粗糙集模型,dubois针对知识是确定的而被近似对象是模糊的情况提出了粗糙模糊集模型。而本文是在知识和被近似对象都是模糊的情况下建立了一个新的变精度模型,首先是将论域进行模糊T划分,得到论域上的各个模糊子集,然后根据模糊的被近似对象与论域上的模糊子集之间的贴进程度关系,得到了新的变精度粗糙模糊集模型,并且研究了算子的有关性质,最后举例说明了该模型的应用。

The extension of rough set model is one of the important contents for researching of rough set. According to the characteristics of knowledge and approximate objects in the domain, the scholars proposed rough set models for different situations, Such as Pawlaw proposed the classical rough set model on the basis of the knowledge and approximate objects are all determined, Dubois proposed the rough fuzzy set model according to the knowledge is determined,but the approximate object is fuzzy. In this paper, a new variable precision model is established in the case of the knowledge and the approximate object are all fuzzy. Firstly we divide the domain into some fuzzy subsets by T-fuzzy partition,and then according to the nearness of knowledge and approximate objects get the variable precision rough fuzzy set model. Besides the properties of the operators are investigated. Finally, an example is given to illustrate its application.