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粗糙集扩展模型的研究是粗糙集理论研究的一个重要问题,在引入了K粒度粗糙关系基础上定义了K粒度粗糙集模型并给出了K粒度分割概念,文章从信息熵的角度给出K粒度粗糙集模型的粗糙熵的不确定度量方法,讨论了该模型随知识分类粒度变化与粗糙熵之间的关系,证明了知识分类粒度呈细分时粗糙熵具有单调递增性,并且以实例验证了此模型理论的有效性与正确性,该模型使得粗糙集处理数据的范围扩展到了实域.在此基础上定义了K粒度模糊粗糙集模型,并研究了其结构及性质,最后给出了K粒度模糊粗糙集模型依参数0<β≤α≤1的扩展模型,并研究了模型的性质与粗糙度之间的关系.
The research of rough set expansion model is an important issue in rough set theory research. Based on the introduction of rough set of K granularity, a K-granular rough set model is defined and the concept of K granular partition is given. From the perspective of information entropy, K The method for estimating the rough entropy of granular rough set model is discussed. The relationship between granularity variation and rough entropy of the model is discussed. It is proved that the rough entropy has a monotone increasing function when the granularity of knowledge classification is subdivided, The validity and correctness of this model theory, the model makes the range of rough set processing data extended to the real domain.On this basis, we define the K-granular fuzzy rough set model, and study its structure and properties, and finally gives the K Particle size fuzzy rough set model is extended by the parameter 0 <β≤α≤1 and the relationship between the properties of the model and the roughness is studied.