基于SVM多类分类算法的模拟电路软故障诊断

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.

SVM-Based Multi-classifying Algorithm for Soft Fault Diagnosis of Analog Circuits

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.