变精度上近似与程度下近似粗糙集模型的正域及其算法  

Positive Region and its Algorithms in Rough Set Model of Variable Precision Upper Approximation and Grade Lower Approximation

作　　者：张贤勇[1] 熊方[2] 莫智文[1] 程伟[3] 

机构地区：[1]四川师范大学数学与软件科学学院,成都610068 [2]四川天一学院信息工程系,成都610100 [3]电子科技大学计算机科学与工程学院,成都611731

出　　处：《计算机科学》2012年第1期248-251,共4页Computer Science

基　　金：国家自然科学基金(11071178);国家自然科学基金青年科学基金(60803028);四川省科技支撑计划(09ZC1838);四川省教育厅青年基金项目(10ZB004)资助

摘　　要：针对变精度近似与程度近似的结合问题及正域的核心地位,组建了变精度上近似与程度下近似粗糙集模型,并定义了其中的正域概念。研究了模型正域与精度量化指标和程度量化指标关联的内涵及意义,得到了模型正域的精确刻画与性质。为了计算模型正域,提出了自然算法与原子算法,并进行了算法分析与算法比较,得到了自然算法与原子算法具有相同的时间复杂性,而原子算法却具有更优的空间复杂性的结论。最后用一个医疗实例对模型正域及其算法进行了分析与说明。变精度上近似与程度下近似粗糙集模型的正域,从膨胀的优势方向完全扩展了经典粗糙集模型的正域,对与精度参数和程度参数相关的必然性知识发现具有意义。Based on the combination of variable precision approximations and grade approximations,as well as the core position of positive region,rough set model of variable precision upper approximation and grade lower approximation was constructed,and positive region in the model was defined.Related to precision and grade quantitative indexes,the connotation and significance of the positive region were investigated,and precise description and some properties were obtained.In order to calculate the positive region,natural and atomic algorithms were proposed and analyzed,and a conclusion was drawn that natural and atomic algorithms have the same time complexity while atomic algorithm has more advantages in space complexity.Finally,a medical example was given to analyze and explain the positive region and the algorithms.Positive region in rough set model of variable precision upper approximation and grade lower approximation has completely expanded positive region in classical rough set model in a perfect direction,and has great values to necessity knowledge discovery related to precision and grade parameters.

关 键 词：人工智能 粗糙集理论 粗糙集模型 变精度近似 程度近似 正域 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]