基于双量化近似空间的粗糙集模型相关研究

项目名称： 基于双量化近似空间的粗糙集模型相关研究

项目编号： No.61203285

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 张贤勇

作者单位： 同济大学

项目金额： 24万元

中文摘要： 近似空间中的量化是粗糙集模型进行扩张与应用的重要元素。目前，相对量化是研究热点，绝对量化进展缓慢，两者结合形成的双量化才刚起步。本项目主要在双量化近似空间中，采用数学方法与粒计算方法，进行粗糙集模型相关研究，阐明基于双量化的不确定性、量化语义、优化计算、粗糙集模型等的形成机理与应用功效，为近似空间中的双量化描述与应用提供思路。研究内容主要包括：（1）建立量化映射，构建近似空间的数学结构，进行知识、概念、规则的不确定性分析；（2）建立双量化的参数描述原则，研究基本语义粒与基本数据粒；（3）研究双量化粗糙集模型的区域粒体系，构建整体粒层次结构，探讨模型的量化语义提取与优化计算；（4）研究量化粗糙集模型的扩张理论，构建具体双量化模型系统并研究其扩张结构，挖掘良性双量化模型实例。通过这些研究，本项目为近似空间中的粒计算与不确定性的关系分析奠定基础，并推进近似空间中关于量化的知识发现发展。

中文关键词： 粗糙集；双量化；粒计算；不确定性；属性约简

英文摘要： Quantification in approximate space is an important element for expansion and application of rough set model. At present, the relative quantification is a research focus, while the absolute quantification makes low progress; meanwhile, double-quantification formed by the relative and absolute quantification just starts. By using mathemaical method and granular computing method, this project mainly makes related study on rough set model in approximate space on the double-quantification; it aims to illustrate formation mechanism and application function of uncertainty, quantitative semantics, optimal computing and rough set model according to the double-quantification, and provide thoughts for description and application of the double-quantification in approximate space. The main study contents are as follows: (1) quantitative mapping and mathematical structure in approximate space are both constructed, uncertainty analyses of knowledge, concepts and rules are made; (2) a principle of parameter description on the double-quantification is provided, and basic semantics granules and basic data granules are both studied; (3) a system of region granules is explored in rough set model on the double-quantification, and the whole hierarchy structure is studied to explore both quantitative semantics extraction and optimal

英文关键词： rough set；double-quantification；granular computing；uncertainty；attribute reduction