Research on Analog Circuit Fault Diagnosis Based on Optimized Neural Network Algorithm

Although many methods have been proposed for fault diagnosis of analog circuits, due to the diversity of fault phenomena, large discreteness of component parameters, and widespread nonlinearity, analog circuit fault diagnosis is not fully mature in both theory and method. Therefore, based on particle swarm optimization (PSO) algorithm and BP neural network combined with wavelet packet fault feature extraction, this study proposes a new method for analog circuit fault diagnosis that improves PSO optimized BP neural network and wavelet packet fault feature extraction. Improving the PSO algorithm by changing the method of obtaining inertia weights in the original PSO algorithm and adopting asynchronous change strategy factors. Ultimately, the training speed and accuracy of the neural network have been effectively improved. The simulation results show that the proposed method reduces the number of iterations while improving accuracy, and improves convergence speed. This provides a more efficient method for fault diagnosis of analog circuits.