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Jingdong book

粒计算及其不确定信息度量的理论与方法

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粒计算及其不确定信息度量的理论与方法

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Content Description

粒计算是当前计算智能研究领域中模拟人类思维和解决复杂问题的新方法,它涵盖了所有有关粒度的理论、方法和技术,是研究复杂问题求解、海量数据挖掘和不确定性信息处理等问题的有力工具。经过十多年的发展,在与多学科交叉研究的过程中,粒计算正逐步形成其特有的研究体系。《粒计算及其不确定信息度量的理论与方法》介绍了粒计算及其不确定信息度量的理论与方法的最新进展,内容涉及不确定信息处理的基本理论,Rough集理论及其不确定信息度量,边界不确定信息的处理-Rough集、Fuzzy集和Vague集理论,基于信息粒度与Rough集的决策细化的理论分析,基于粒计算的知识约简,基于粒计算的基因表达谱数据挖掘研究,基于粒计算的图像检索,时间序列下的粒度决策演化模型等。
《粒计算及其不确定信息度量的理论与方法》可供计算机、模式识别与智能系统、自动化等相关专业的研究人员、教师、研究生、高年级本科生和工程技术人员参考。
Catalogue

前言
第1章 不确定信息处理的基本理论
1.1 Rough集理论
1.1.1 信息系统与决策信息系统
1.1.2 近似集及其性质
1.1.3 Rough集理论中的近似度量方法
1.1.4 决策表的属性约简
1.1.5 不完备信息系统中Rough集理论的扩充
1.2 Fuzzy集理论
1.2.1 Fuzzy集的定义与表示法
1.2.2 FuzzY集的基本运算与性质
1.2.3 Fuzzy集的其他运算
1.2.4 Fuzzy性的度量
1.2.5 Fuzzy集的推广
1.3 Vague集理论
1.3.1 Vague集的基本概念
1.3.2 Vague集的性质
1.3.3 Vague集的运算规则
1.4 商空间理论
1.4.1 商空间模型的建立
1.4.2 商空间粒度的获得
1.4.3 商空间方法的基本原理——保假原理、保真原理、商逼近
1.5 粒计算理论
1.5.1 粒
1.5.2 粒结构
1.5.3 粒计算的基本问题
1.5.4 粒计算的研究方法与方向
1.6 本章小结
参考文献

第2章 Rough集理论及其不确定信息度量
2.1 Rough集之间的相似性度量研究
2.1.1 Rough集与限制容差关系
2.1.2 基于不可分辨关系的经典Rough集之间的相似性度量
2.1.3 基于限制容差关系的广义Rough集之间的相似性度量
2.1.4 经典Rough集与广义Rough集中集合之间相似性度量的理论统一
2.2 基于Rough集的信息系统中各种基本信息的度量
2.2.1 信息系统中属性重要性的度量方法及其理论
2.2.2 属性值间相异度度量及其理论分析
2.2.3 信息系统中对象之间的相似性度量及其理论分析
2.3 信息系统中知识粒度与粒度熵理论
2.3.1 知识的粒度原理
2.3.2 知识的粒度熵原理
2.3.3 知识的粒度和粒度熵之间的关系
2.4 信息系统中知识距离与知识贴近度理论
2.4.1 信息系统中的知识距离
2.4.2 信息系统中的知识贴近度
2.4.3 基于知识距离的属性相关性度量理论研究
2.5 基于粒计算的相似性度量理论
2.5.1 问题的提出与贴近度的基础知识
2.5.2 基于粒计算的贴近度理论
2.5.3 基于粒计算的格贴近度理论
2.6 信息系统中的概念粒及其不确定性度量
2.6.1 概念粒的表示方法及其运算规则
2.6.2 概念粒的距离及其性质
2.6.3 概念粒的内涵重要度
2.7 一种新的粒表示方法及其不确定性度量
2.7.1 信息系统中粒的新表示方法及其运算
2.7.2 粒的距离计算及其性质
2.7.3 基于粒距离的相似性度量
2.8 信息系统中的粒结构分析
2.8.1 粒格的表示及其运算规则
2.8.2 粒格与概念格的比较
2.8.3 粒的分层结构的分析方法
2.9 本章小结
参考文献

第3章 边界不确定信息的处理——Rough集、Fuzzy集和Vague集理论
第4章 基于信息粒度与Rough集的决策细化的理论分析
第5章 基于粒计算的知识约简
第6章 基于粒计算的基因表达谱数据挖掘研究
第7章 基于粒计算的图像检索
第8章 时间序列下的粒度决策演化模型

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