基于粒度的粗集-决策树雷达信号识别模型  

Recognition Model of Radar Signal Based on Rough Set with Granulation and Decision Tree

作　　者：陈婷[1] 罗景青[1] 

机构地区：[1]解放军电子工程学院,安徽合肥230037

出　　处：《微电子学与计算机》2008年第12期13-16,共4页Microelectronics & Computer

基　　金：国家"八六三"计划项目(2005AA775020)

摘　　要：通过引入知识粒度的概念,对信息系统中属性的重要度进行了定义,并以属性重要度为启发式信息,进行粗集的属性约简.在构造决策树的过程中,基于粗集的理论运用了加权平均粗糙度的概念,并将其作为选择分离属性的标准.将这种联合粗集与决策树的模型应用到雷达信号识别中,经实验证明,用该方法构造的决策树复杂性低,且能有效提高分类效果.The significance of attribute in information s^tem is defined by applying the concept of granulation; then the significance of attribute can be treated as the heuristic information to attribute reduction in rough set. In the process of constructing a decision tree, weighted mean roughness, a new concept based on rough set theory which is regarded as the criteria for choosing attributes is applied. The model which combines rough set and decision tree is used in radar signal recognition and the experiments show that the decision tree constructed in this paper is simpler in structure, and can improve the efficiency of classification.

关 键 词：粒度 属性约简 粗集 决策树 加权平均粗糙度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]