Fault Diagnosis of Electric Power Communication Network Based on Particle Swarm Optimization BP Neural Network
In order to improve the accuracy of fault diagnosis results in power communication networks,this paper proposes a fault diagnosis method for power communication networks based on particle swarm optimization BP neural network.The PSO algorithm is used to optimize the BP neural network and establishes a PSO-BPNN fault di-agnosis model.The sample data generated by the power communication network testing system is used for simula-tion analysis,and compared with other methods.The results showed that the PSO-BPNN model proposed in this pa-per only experienced two false diagnoses during the diagnosis process,and the accuracy rate of the diagnosis results is as high as 97.22%.The diagnosis effect is better,that verifying the effectiveness and practicality of the proposed method.
power communication networkfault diagnosisparticle swarm optimization algorithmBP neural network
万方数据
维普
