基于云模糊组的模拟电路故障字典测点选择  

Test Point Selection of Analog Circuit Fault Dictionary Based on Cloud-Ambiguity-Group

作　　者：刘琦[1,2] 赵红东[1] 周锦姝[2] 戴华林[2] 

机构地区：[1]河北工业大学信息工程学院,天津300401 [2]天津城建大学计算机与信息工程学院,天津300384

出　　处：《计算机测量与控制》2013年第11期2908-2911,共4页Computer Measurement &Control

基　　金：天津市自然科学基金项目(10JCYBJC00600);河北省自然科学基金项目(F2013202256)

摘　　要：通过对模拟电路故障字典的测点选择问题进行研究,针对目前普遍的将"0.7V"等类似的绝对定量数值作为测点选择中模糊组划分依据的研究现状,文章提出一种适用于测点选择的基于云模糊组的整数编码故障字典方法;该方法利用云模型理论的逆向云算法和云变换算法将模糊组的划分依据由绝对的定量数值转换为相对的定性概念,利用云模型理论的相似云算法进行模糊组编号概念的隶属判定来构造云模糊组,从而实现构建模拟电路的概念故障字典;通过对具体电路的仿真实验与结果分析表明,该方法充分考虑了模拟电路故障状态的模糊性、随机性等不确定性因素,使用云模型特征值(Ex,En,He)代替"0.7V",得到了较理想的优化测试点集。After studying the test point selection problem of analog circuit fault dictionary, aiming at the current research status that 0. 7V, an absolute quantification, often is used as the division reference of the ambiguity group in some test point selection methods, this paper presents a test point selection method of integer coding fault dictionary based on cloud model theory. Using backward cloud algorithm and cloud transformation algo- rithm, this method achieves the conversion of division reference on ambiguity group from the absolute quantitative value into the relative qualitative con- ception. And using similar cloud algorithm, this method achieves the cloud--ambiguity--group of analog circuit fault dictionary by performing the membership determination of ambiguity group conception. The simulation analysis of the specific circuit shows that, this method fully considers the an alog circuit fault condition＇s uncertain factors such as fuzziness and randomness, and get a better optimize test point set under this method by using cloud model characteristic value （Ex, En, He） instead of 0. 7V.

关 键 词：云模糊组 云模型 测点选择 故障字典 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]