现代电子技术2016，Vol.39Issue(19)：161-164,4.DOI:10.16652/j.issn.1004-373x.2016.19.039
人工鱼群算法选择特征和加权的模拟电路故障诊断
Analog circuit fault diagnosis based on feature and weighting selection of AFSA
摘要
Abstract
In order to accurately track the changing characteristics of analog circuit fault,an analog circuit fault diagnosis model based on feature and weighting selection of artificial fish swarm algorithm(AFSA)is put forward. The original feature set of the analog circuit state is obtained according to the Volterra series. The relevant vector machine is adopted as the classifier of the analog circuit fault. The artificial fish swarm algorithm is used to select the important feature subset,and give a rational weight for each feature. The model was applied to a certain analog fault circuit. The results show that the artificial fish swarm al⁃gorithm can get the optimal feature subset accurately,the analog circuit fault rate is averagely higher than 95%,and the perfor⁃mance of the model is significantly superior to the classical model.关键词
模拟电路/特征选择/特征加权/人工鱼群算法Key words
analog circuit/feature selection/feature weighting/artificial fish swarm algorithm分类
信息技术与安全科学引用本文复制引用
方伟骏,黄圣国..人工鱼群算法选择特征和加权的模拟电路故障诊断[J].现代电子技术,2016,39(19):161-164,4.基金项目
江苏省教育厅高校科研成果产业化推进项目（JHB2011-75）；淮安市市级科技支撑项目 （）
