摘要
标准的粗糙集理论不能很好地处理带有噪声的数据,而故障诊断信息中难以避免地存在噪声数据,对此,提出了一种基于变精度粗糙集理论的故障诊断模型。先用自组织特征映射神经网络对连续属性进行离散化,然后利用变精度粗糙集的近似依赖性进行属性约简,据此得到决策规则,并给出了一个实例来说明如何应用这种故障诊断模型。
The standard rough set theory cannot effectively process the noise data, but there is always noise data in fault diagnosis data.Accordingly, a model of fault diagnosis based on VPRS （variable precision rough set） theory is proposed, the approach is realized by applying SOM （self-organizing map neural network） to discretize continuous attributes, using property of approximation dependency of VPRS to carry through attribute reduction and concluding decision-making rules. An example is given to explain how to use the fault diagnosis model.
出处
《计算机工程与设计》
CSCD
北大核心
2009年第3期657-659,共3页
Computer Engineering and Design
基金
江苏省教育厅自然科学基金项目(05KJB520048)
关键词
变精度粗糙集
故障诊断
离散化
属性约简
决策规则
variable precision rough set
fault diagnosis
discretization
attribute reduction
decision-making rule
