不确定信息的粗糙集表示和处理  

Uncertain knowledge representation and processing based on rough set

作　　者：王国胤[1] 苗夺谦[2] 吴伟志[3] 梁吉业[4] 

机构地区：[1]重庆邮电大学计算机科学与技术研究所,重庆400065 [2]同济大学电子与信息工程学院,上海201804 [3]浙江海洋学院数理与信息学院,浙江舟山316000 [4]山西大学计算机与信息技术学院,山西太原030006

出　　处：《重庆邮电大学学报（自然科学版）》2010年第5期541-544,550,共5页Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)

基　　金：supported by National Natural Science Foundation of P.R.China(60773113);The Science & Technology Research Program of the Municipal Education Committee of Chongqing of China(KJ060517);The Natural Science Foundation Project of CQ CSTC(2008BA2017;2008BA2041)

摘　　要：随机性和模糊性是不确定性中最重要和最基本的2个特征。分析和比较了表示和处理不确定性知识的一些主要的拓展集合理论,并系统的介绍了基于粗糙集的不确定知识的表示和处理方法。系统讨论了粗糙集理论对经典集合论的拓展,用经典集合计算方法对粗糙集的核心算子进行了对比分析,同时对定义在信息系统上的粗糙逻辑也进行了分析。通过分析粗糙集理论在人工智能领域的几类典型应用案例说明了粗糙集在表示和处理不确定性问题方面的重要作用和优势。最后对不确定知识的表示和处理的一些有待进一步深入研究的关键问题进行了展望。The randomness and fuzziness are the most important and basic for the uncertainty characteristics. Some major expansion of set theory about uncertain knowledge representation and processing are analyzed and compared in this paper. There is a systematic introduction of uncertainty based on rough set knowledge representation and processing methods. Ex- tension of rough set theory based on the classical set theory is discussed deeply. Core operators of rough set were analyzed based on the classical set theory. At the same time, rough logic defined on the information systems is analyzed. It is proved that the important role and advantages of rough set theory for representing and dealing with the uncertainty based on the typical cases in the field of artificial intelligence. Finally, key issues about further studying on uncertain knowledge representation and processing are also discussed.

关 键 词：不确定性 粗糙集 算子 粗糙逻辑 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]