ABSTRACT As an important model in the field of artificial intelligence, an information system is a database that stands for relationships between objects and attributes. A distributed fully fuzzy information system is an information system with distributed fully fuzzy data. This paper investigates measures of uncertainty for a distributed fully fuzzy information system. The fuzzy -equivalence relation, induced by a fully fuzzy information system by using Gaussian kernel method, is first obtained. Then, fuzzy information structures in a distributed fully fuzzy information system is introduced. Next, Dependency between fuzzy information structures is depicted from three aspects, information distance for calculating the difference between fuzzy information structures is proposed in the same distributed fully fuzzy information system. Moreover, properties of fuzzy information structures in a distributed fully fuzzy information system are given by means of the inclusion degree. Finally, granulation measure and entropy measure of a given distributed fully fuzzy information system is proposed by means of its fuzzy information structures. These results will be very helpful for establishing a framework of granular computing and understanding the essence of uncertainty in distributed fully fuzzy information systems.

中文翻译：

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分布式完全模糊信息系统的不确定性测度

摘要 作为人工智能领域的一个重要模型，信息系统是一个代表对象和属性之间关系的数据库。分布式全模糊信息系统是具有分布式全模糊数据的信息系统。本文研究了分布式完全模糊信息系统的不确定性度量。模糊等价关系是由一个完全模糊的信息系统通过高斯核方法得到的。然后，介绍了分布式全模糊信息系统中的模糊信息结构。接着，从三个方面描述了模糊信息结构之间的依赖关系，提出了在同一个分布式全模糊信息系统中计算模糊信息结构之间差异的信息距离。而且，分布式全模糊信息系统中模糊信息结构的性质通过包含度给出。最后，利用给定的分布式全模糊信息系统的模糊信息结构，提出了其粒度测度和熵测度。这些结果对于建立粒计算框架和理解分布式完全模糊信息系统中不确定性的本质将非常有帮助。