几种粗糙集模型推广的研究
发布时间：2019-02-26 20:53
【摘要】：粗糙集理论是解决含有不完备性信息问题的重要理论知识,粗糙集模型的推广程度对于处理不确定性信息问题起到关键作用。为此,本文从下面三个角度对模型进行研究:1.有关覆盖的粗糙集模型的研究已经取得较为丰富的成果,本文以覆盖粗糙模糊集模型为基础,将模型从单个论域推广至双论域上进行研究,并且讨论了所提出新模型的算子性质,从而推广和丰富了有关覆盖的粗糙集模型体系。2.基于粗糙集与模糊集相结合的模型,并且在知识集和被近似集同时都是模糊的情况下,提出新的模型。具体是先将论域进行模糊划分得到相应的模糊知识集,然后结合模糊贴近度的概念提出新的粗糙集模型,并对模型的有关算子的性质进行了研究,最后举例说明新建立模型在实际生活中的运用。3.处理具有不完备性的信息系统是粗糙集应用的主要内容之一,而在不同关系下,将粗糙集模型进行推广,对于解决问题起到关键作用。本文根据一种改进的限制容差关系,建立了一种新的多粒度粗糙集模型,并对该模型算子的相关性质进行了讨论,而且研究了新建立模型与基于以上三种关系模型的内在关系,最后通过处理不完备信息表说明了新建立模型的优点。
[Abstract]:Rough set theory is an important theoretical knowledge to solve problems with incomplete information. The generalization of rough set model plays a key role in dealing with uncertain information problems. To this end, this paper studies the model from the following three perspectives: 1. The research on covered rough set model has achieved a lot of achievements. Based on the covered rough fuzzy set model, this paper extends the model from a single universe to a double universe. Furthermore, the operator properties of the proposed new model are discussed, which generalizes and enriches the covering rough set model system. Based on the combination of rough set and fuzzy set, a new model is proposed when both the knowledge set and the approximate set are fuzzy at the same time. Firstly, the fuzzy domain is divided into corresponding fuzzy knowledge sets, and then a new rough set model is proposed based on the concept of fuzzy closeness, and the properties of the operators of the model are studied. Finally, an example is given to illustrate the application of the new model in real life. 3. The processing of incomplete information system is one of the main contents of rough set application. Under different relations, the extension of rough set model plays a key role in solving the problem. In this paper, a new multi-granularity rough set model is established based on an improved restricted tolerance relation, and the related properties of the operator of the model are discussed. Furthermore, the intrinsic relationship between the new model and the model based on the above three relations is studied. Finally, the advantages of the new model are illustrated by dealing with incomplete information tables.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18
本文编号：2431136
[Abstract]:Rough set theory is an important theoretical knowledge to solve problems with incomplete information. The generalization of rough set model plays a key role in dealing with uncertain information problems. To this end, this paper studies the model from the following three perspectives: 1. The research on covered rough set model has achieved a lot of achievements. Based on the covered rough fuzzy set model, this paper extends the model from a single universe to a double universe. Furthermore, the operator properties of the proposed new model are discussed, which generalizes and enriches the covering rough set model system. Based on the combination of rough set and fuzzy set, a new model is proposed when both the knowledge set and the approximate set are fuzzy at the same time. Firstly, the fuzzy domain is divided into corresponding fuzzy knowledge sets, and then a new rough set model is proposed based on the concept of fuzzy closeness, and the properties of the operators of the model are studied. Finally, an example is given to illustrate the application of the new model in real life. 3. The processing of incomplete information system is one of the main contents of rough set application. Under different relations, the extension of rough set model plays a key role in solving the problem. In this paper, a new multi-granularity rough set model is established based on an improved restricted tolerance relation, and the related properties of the operator of the model are discussed. Furthermore, the intrinsic relationship between the new model and the model based on the above three relations is studied. Finally, the advantages of the new model are illustrated by dealing with incomplete information tables.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18
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