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

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