摘要
在标准Rough集理论的指导下,利用偏序关系性质构造了不同分类,并以此为基础探讨了上、下近似集,从而构建了基于偏序关系的Rough集模型。新模型将Rough集理论的应用范围由等价关系扩展到偏序关系。为了更好地增强模型的实用性和灵活性,一方面从程度、精度、概率等角度出发分别对其进行了扩展,另一方面引入依赖度使其适用于研究各种非严格的偏序关系。给出了实例分析,并结合现实生活中的现象阐述了模型的应用价值。
According to the standard rough set theory, different classifications are made by the properties of partial order relation, then to represent lower and upper approximation set, and make rough set model based on partial order relation. The new model under partial order extends the application of rough set theory on equivalence relation. In order to strengthen its practicality and flexibility, the model is expanded in grade, precision, probability and so on, otherwise introduce the dependence degree to make it adapt to nonstandard partial order relation. In the end of the paper, some examples are given, and to show the applying value with analyzing true- life phenomena
出处
《南昌大学学报（理科版）》
CAS
北大核心
2006年第6期608-612,共5页
Journal of Nanchang University(Natural Science)
基金
国家自然科学基金资助项目(60173054)
江西省自然科学基金资助项目(Z01416)
关键词
ROUGH集
偏序关系
偏序类
覆盖
依赖度
rough set
partial order relation
partial order classification
coverage
dependence degree
