【作者】 罗英；

【导师】 张洪伟；

【作者基本信息】 四川大学 ， 计算机软件与理论， 2006， 硕士

【摘要】 评估决策是通过评估信息的加工形成综合排序的一种选择行为,己在企业、教育、科技管理中得到了广泛应用。在评估决策系统中,不确定性由于信息与数据的增加变得十分显著。粗糙集理论是在对不确定和不完全信息的分类和数据处理实践中由Pawlak提出来的。粗糙集理论最初在人工智能的某些分支,例如推理,自动分类,模式识别,学习算法等的研究中是很重要的。近年来,随着粗糙集理论的发展,粗糙集理论推动了分类理论,聚类分析,度量理论,决策支持等理论的研究和应用。粗糙集理论的关键是建立两个被称为下、上近似的子集来近似表示论域上的任意集合。粗糙集理论中的有效算法研究是粗糙集理论的一个研究热点,目前主要集中在决策规则提取算法,属性约简算法,粗糙集基本并行算法,以及与粗糙集有关的神经网络和遗传算法等。其中,属性约简算法和规则提取算法是粗糙集理论及应用研究的重要内容。包含度是各种不确定性推理方法的概括。它是包含关系的定量描述,它包容了“关系”的不确定性。包含度理论同模糊集理论相辅相成,成为研究不确定性的重要工具。由于现实世界中信息的不确定性,信息系统的条件属性和目标属性都有可能是模糊的,因此,本文结合粗糙集与包含度的概念,在模糊信息系统中进行评估决策。首先,本文将包含度概念引入到粗糙集理论中,建立了包含度与粗糙集数据分析中的度量之间的关系,证实了粗糙集数据分析中的有关度量均可归结为包含度。然后,通过以下步骤将这两个概念同时用于模糊信息系统中:1)利用Pawlak粗糙集理论和表现定理得到一个新的粗糙模糊集模型;2)利用包含度给出决策规则的提取和属性约简的方法;3)结合遗传算法在决策规则与条件属性已知的情况下给出目标属性值。本文的研究结果在DRP系统下的经销商管理模块得到很好的应用,有效地辅助了企业决策者选择和评估经销商。更多还原

【Abstract】 Assessment is a process of making judgment in which information is synthesized to determine rankings. Assessment is a routine task in business management, education administration, and research and development administration. In the assessment decision system, uncertainty becomes very obvious due to the rapidly increase of the information and data. The theory of rough sets is proposed by Pawlak in practice of classification and data analysis with uncertain and incomplete information. The rough set concept can be of some importance, primarily in some branches of artificial intelligence, such as reasoning, automatic classification, pattern recognition, learning algorithms, etc. In recent years, with the development of rough set theory, the idea of rough set leads to further research and applications in classification theory, cluster analysis, measurement theory, decision-supporting, etc. The key of the rough set theory deals with the approximation of an arbitrary subset of a universe by two subsets called lower and upper approximations. Many researchers pay attention to the study of the algorithm using rough set approach, such as generation of decision rule algorithm, reduction of attributes algorithm, the parallelism algorithm and the algorithm associated the neural networks or GA with rough set and so on. The attributes reduction and decision rule generation are the kernel contents of rough set theory. Inclusion degree theory presents a unified account of various quantitative methods for uncertainty reasoning. Inclusion degree is quantitative description of containment relation. It holds the uncertainty of the relation. The inclusion degree theory and the fuzzy set theory are the important tools in studying the uncertain knowledge.Due to the uncertainty of information in the real world, the condition attributes and objective attributes could be fuzzy, therefore, this paper processes the assessment ranking in the fuzzy information system combining the concept of rough set and更多还原