全样本支持矢量数据描述模拟电路故障分类

摘要

传统支持矢量数据描述（Support Vector Domain Description,SVDD）分类规则完全忽略非支持矢量所包含的样本信息,为此本文提出了一种全样本SVDD分类方法,并应用到模拟电路故障诊断中。该方法以贝叶斯理论和分类器模糊融合思想为基础,利用核密度估计得到的类条件概率密度和先验概率的乘积对SVDD相对距离进行加权。实验结果表明,与SVM扩展的多分类器相比,本文方法能够有效提高模拟电路故障诊断的准确率,且全样本SVDD分类模型对参数变化具有较强的稳健性。%The information included in the non-support vectors is completely ignored for the classification rules of the classical support vector domain description（SVDD）,so an all samples SVDD method is proposed in this paper,and it is applied to analog circuit fault diagnosis.The new method is based on Bayes theory and classifier fuzzy fusion strategy.The relative distances of this classifier are weighted by the product of prior probability value and conditional probability value,which are calculated by kernel density estimation.The simulation results show that,compared with the multi-class SVM classifiers,the introduced method improves the fault diagnosis accuracy of analog circuit.Moreover,the all samples SVDD classifier is robust against the changes of classifier parameter.