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
维普
万方数据
