摘要：Regarding to the complexity and diversity of analog circuit fault, a principal component analysis(PCA) and particle swarm optimization(PSO) support vector machine(SVM) analog circuit fault diagnosis method is proposed. It uses principal component analysis and data normalization as preprocessing, then reduced dimension fault feature is putted into support vector machine to diagnosis, and particle swarm optimization is used to optimize the penalty parameters and the kernel parameters of SVM, that improve the recognition rate of the fault diagnosis. The simulation results show that the proposed diagnosis model can perform analog circuit fault diagnosis effectively, and has higher fault diagnosis rates
