基于粗糙集的烤烟烟叶智能分级方法  

Intelligent Grading of Flue-cured Tobacco Leaves Based on Rough Set Theory

作　　者：谭旭[1] 唐云岚[2] 陈英武[2] 

机构地区：[1]深圳信息职业技术学院计算机应用系,深圳518029 [2]国防科技大学信息系统与管理学院,长沙410073

出　　处：《农业机械学报》2009年第6期169-174,共6页Transactions of the Chinese Society for Agricultural Machinery

摘　　要：针对烤烟烟叶的分级问题,提出了一种基于粗糙集理论的智能分级方法。为了适应烟叶分级的特点,将粗糙集理论予以扩展和改进,给出了相应的离散化和属性约简算法。同时构造了烟叶分组、分级的二级推理模型,以实现合理粒度的知识获取。通过烟叶的化学指标进行"部位"和"颜色"上的并行分组,可得到各自的推理规则以及影响指标和相应的重要度。在规则推理获得烟叶的分组后,借助所有化学指标的重要度进行多属性决策判定得到烟叶的分级。实验结果表明该方法有效可靠,并优于同类其他方法。Aiming at grading flue-cured tobacco leaves, an intelligent method based on rough set theory was proposed. Traditional rough set theory was generalized for the characteristics of flue-cured tobacco leaves＇ grading, and relevant algorithms of discretization and attribute reduction based on rough set theory were also given. In order to obtain knowledge with reasonable granularity, a two-level reasoning model that consists of grouping and grading was constructed. By parallel grouping according to leaves＇ positions and colors based on chemical compounds, inference rules and importance degree of each chemical compound can be obtained. Then, through multi-attribute evaluation found on the importance degree and the group inferred from the rules, final grade of the given flue-cured tobacco leaf was determined. Experiment results prove that this method is effective and credible, and has advantages over the previously proposed methods.

关 键 词：烤烟烟叶 分级 化学指标 粗糙集 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TS47[自动化与计算机技术—控制科学与工程]