Analog Circuit Fault Diagnosis Based on Rough Set and LVQ

摘要：

　　In order to solve the difficulty of recognition in analog circuit fault diagnosis,under the two aspects of analog circuit fault feature extraction and fault pattern recognition,combined with their respective characteristics of rough set theory (RS) and the learning vector quantization (LVQ) neural network,a new analog circuit fault diagnosis method based on rough set and learning vector quantization is proposed in this paper.The RS method is used in analog circuit fault feature dimension reduction,classification and the application of LVQ network in fault mode.Simulation results show that,under the same precision request,this algorithm training time is far smaller than the ordinary evolution neural network,and this method has certain practical significance in the fault diagnosis for analog circuits.

关键词： Neural network LVQ Rough set Fault diagnosis Analog circuit

作者: Shi-Guan ZHOU Zai-Fei LUO Guo-Jun LI Yan ZHENG

作者单位: Ningbo University of Technology,Ningbo,Zhejiang,China

会议类型: 国内会议

会议名称: 2014年国际计算机科学与软件工程学术会议

会议地点: 杭州

会议语种:英文

页码: 1-6

在线出版日期: 2014-10-18（万方平台首次上网日期，不代表论文的发表时间）