东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2008, Vol. 29 ›› Issue (7): 924-927.DOI: -

• 论著 • 上一篇    下一篇

基于SVM多类分类算法的模拟电路软故障诊断

王安娜;邱增;吴洁;曲福明;   

  1. 东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院;东北大学信息科学与工程学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2008-07-15 发布日期:2013-06-22
  • 通讯作者: Wang, A.-N.
  • 作者简介:-
  • 基金资助:
    辽宁省自然科学基金资助项目(20062033);;

SVM-based multi-classifying algorithm for soft fault diagnosis of analog circuits

Wang, An-Na (1); Qiu, Zeng (1); Wu, Jie (1); Qu, Fu-Ming (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2008-07-15 Published:2013-06-22
  • Contact: Wang, A.-N.
  • About author:-
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摘要： 给出了基于支持向量机(SVM)1-v-1和决策导向无环图(decision directed acyclic graph,DDAG)多类分类算法的模拟电路软故障诊断新方法.DDAG是在1-v-1算法基础上构建的新的学习架构,在对多个SVM子分类器进行组合的过程中,引入了图论中有向无环图的思想.比较了采用不同核函数时支持向量机的分类结果.实验结果表明采用DDAG支持向量机(DAGSVM))多类分类算法时,诊断准确率为99%.因此,DAGSVM算法具有较高的诊断准确率.

关键词: 模拟电路, 支持向量机, 软故障诊断, 核函数, 决策导向无环图

Abstract: Based on the support vector machine (SVM) 1-v-1 and DDAG (decision directed acyclic graph) multi-classified algorithm, a new approach to the soft fault diagnosis of analog circuits is presented. The DDAG is a newly developed learning system on 1-v-1 basis in which the idea of directed acyclic graph of the graph theory is introduced to combine SVM subclassifiers together. Then, the SVM classification results by using different kernel functions are compared experimentally with each other. The simulation results show that the diagnosis accuracy is up to 99% if using the DDAG support vector machine algorithm. In this way the higher diagnostic accuracy is available.

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