基于串行支持向量分类器的模拟电路故障诊断

Analog circuits fault diagnosis based on serial support vector multi-classifier

介绍了支持向量机的基本原理 ,提出一种新型支持向量多类分类器 ,其中多个二类分类器组成串行结构 ,每个二类分类器均带有非线性主元素分析特征提取器 .描述了其训练与分类算法 ,并将其应用于非线性电路的部件级诊断 .和传统BP网和RBF网分类器相比 ,支持向量方法在分类准确率上表现出明显的优势 ,其中串行支持向量多类分类器无论在训练和分类速度方面 ,还是在诊断准确率方面 。

A support vector multiclassification methodology was proposed. Several binary support vector binary classifiers, each of which equipped with a feature extractor based on kernel principle components analysis, were organized in a serial structure. Its training process and classification algorithm were described. The BP net classifier, RBF net classifier, traditional support vector multi-classifier and serial support vector multi-classifier (SSVC) were used for analog circuit fault diagnosis. Compared with BP net and RBF net classifiers, support vector approach has significantly better classification accuracy on test patterns. The SSVC affords top diagnosis accuracy among these classifiers and outperforms traditional support vector multi-calssifier dramatically in training and classification efficiency.