Abstract: In order to improve fault diagnosis rate of analog circuits, this paper proposes a fault diagnosis method based on jointly selection features and classifier parameters model. The features and classifier parameters are encoded as a particle, and then, the optimal features and classifier parameters are obtained by the particle swarm optimization algorithm according to objection function, and the samples are reduced on the optimal features. Finally, the samples are input into the classifier to train and build the fault diagnosis model of analog circuits with the optimal parameters, and the simulation experiments are carried out to test the performance of the model. The results show that the proposed method can select the optimal features and classifier parameters quickly to improve the fault diagnosis rate of analog circuits and fasten the speed of the fault diagnosis compared with other methods.

Key words: analog circuits, fault diagnosis, features selection, classifier parameters, particle swarm optimization algorithm

摘要： 为了提高模拟电路故障诊断准确率，提出一种联合选择特征选和分类器参数模型的模拟电路故障诊断方法（Feature-Classifier）。将模拟电路故障特征子集和分类器参数编码成为粒子，然后粒子根据目标函数通过信息交流和互相协作找到最优特征子集和分类器参数，并根据最优特征子集对样本进行约简；分类器根据最优参数对约简后样本进行训练建立模拟电路故障诊断模型，并通过仿真实例对性能进行测试。结果表明，相对于其他模拟电路故障诊断方法，Feature-Classifier能够较快找到最优特征子集与分类器参数，不仅提高了模拟电路故障诊断准确率，并加快了故障诊断速度。

关键词: 模拟电路, 故障诊断, 特征选择, 分类器参数, 粒子群优化算法