摘要本文在理论研究的基础上，利用粗糙集理论解决案例决策方面的问题，以说明粗糙集在案例决策和智能信息处理过程中的实际应用意义。

本文研究了粗糙集理论的基本概念。讨论了粗糙集理论提出的背景、集合的等价关系、信息系统观点及粗糙集上、下近似的定义。研究了变精度粗糙集模型和决策表约简中有关案例属性约简的方法，主要分为离散型案例属性约简算法和连续型案例属性值约简算法俩大类。为了说明粗糙集理论在智能信息决策方面的实际应用性，本文将粗糙集理论应用于物流配送中心道路通行能力与需求匹配的实际案例中，基于案例推理（CBR）技术原理与粗糙集理论的结合，对模拟数据进行属性约简，得出最简决策表，从而提高智能信息处理的效率。论文为粗糙集理论和案例约简的应用提供了理论基础和可借鉴的实例。63656

毕业论文关键词  粗糙集  属性约简  可变精度粗糙集  属性离散化

毕 业 论 文 外 文 摘 要

Title  Based on variable precision rough set reduction method case study

Abstract  In this paper, based on the theoretical research, we ues the rough set theory to solve the problem of decision-making scenarios, in to reveal the significance of the rough set in the decision-making case and intelligent information processing of practical applications.

At the beginning, this paper studies the basic concepts of rough set theory, discusses the background of rough set theory, a collection of equivalence relations, information systems perspective and rough set upper and lower approximation definitions. Then study the Decision Table Reduction in relevant cases attribute reduction method which is pided into discrete case attribute reduction algorithms and continuous case attribute value reduction algorithm. Finally, to illustrate the rough set theory used in the intelligent decision-making aspects in the practical situation, this paper put the rough set theory into the actual case of logistics distribution center road capacity and demand match. Based on the combining of CBR techniques and Rough Set Theory, we made an attribute reduction for the stimulating data, and finding the most simple decision table, thereby improving the efficiency of intelligent information processing. This paper provided the theoretical basis and practical examples for the application of rough set theory and case reduction.

Keywords  Rough set  Attributes reduction  Variable precision rough set Discretization of attributes 

1  引言 1

1.1 研究背景 1

1.1.1粗糙集理论研究现状 1

1.1.2  粗糙集理论在案例检索中的应用 2

1.2 本文主要研究内容 3

2 粗糙集理论的基本概念 4

2.1 粗糙集理论的概述 4

2.2 变精度粗糙集概述 5

2.3 集合间的等价关系 5

2.4 知识库的形式化定义 6

2.5 信息系统的定义 7

2.4.1  信息系统的表示 7

2.4.2  知识的不可辨识关系 8

2.6 粗糙集的定义 9

2.6.1粗糙集的下近似和上近似 9

2.6.2 粗糙集的扩展概念 12

2.6.3 新型的隶属关系