关键词: 模拟电路, 故障诊断, RBF神经网络, 小波分解, 狼群算法

Abstract: This paper proposes a new method for simulating circuit fault diagnosis. The method includes wavelet decomposition of Haar, normalization of data, and optimization of RBF neural network by wolf pack algorithm. Firstly, the original fault data set of the obtained circuit is transformed by Haar wavelet, and then the transformed data is normalized, and finally the input data needed for RBF neural network training is obtained. This paper uses wolf pack algorithm to optimize RBF neural network to improve the stability of network training and the success rate of diagnosis for RBF neural network. Finally, the implementation process of these methods is discussed through the diagnosis of two circuits, and the feasibility of the simulated circuit fault diagnosis is verified by using this method.

Key words: analog circuit, fault diagnosis, RBF neural network, wavelet decomposition, wolf pack algorithm