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In order to improve the speed and accuracy of analog circuit fault diagnosis,using Back Propagation Neural Network(BPNN),a new method is proposed based on Particle Swarm Optimization(PSO)to adjust weights of BP neural network.The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm,but also improve the learning ability and generalization ability with a higher precision.The response signals of analog circuit is preprocessed by Wavelet Packet Transform(WPT)as the fault feature.The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed,which is effective for fault location.
In order to improve the speed and accuracy of analog circuit fault diagnosis, using Back Propagation Neural Network (BPNN), a new method is proposed based on Particle Swarm Optimization (PSO) to adjust weights of BP neural network. The model can not only overcome the limitations of the slow convergence and the local extreme values by basic BP algorithm, but also improve the learning ability and generalization ability with a higher precision. The response signals of analog circuit is preprocessed by Wavelet Packet Transform (WPT) as the fault feature. The simulation result shows that the proposed method has higher diagnostic accuracy and faster convergence speed, which is effective for fault location.