摘要
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 pre- cision. 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 high- er diagnostic accuracy and faster convergence speed, which is effective for fault location.
出处
《沈阳理工大学学报》
CAS
2014年第5期90-94,共5页
Journal of Shenyang Ligong University
基金
supported the Science and Technology Research Project of Liaoning Provincial Department of Education
关键词
错误判断
BP神经式网络
颗粒群最佳化
模拟线路
fault diagnosis
BP neural network
particle swarm optimization
analog circuit
