Research on Troubleshooting of Communication Equipment Based on PSO-BP

摘要

In order to improve the efficiency and intelligent level of handling communication equipment failures for maintenance personnel at base station, BP neural network algorithm and particle swarm optimization algorithm are used to construct three kinds of communication equipment fault diagnosis models to predict the possible equipment failure type, which helps guide the base station maintenance personnel to eliminate faults in a targeted manner, reducing human error effectively and saving equipment troubleshooting time greatly. The simulation results of Matlab show that the proposed method effectively enhances BP algorithm's ability to deal with nonlinear problems, and improves the convergence speed of BP algorithm and the ability to search for global optimal values. Applying the neural network improved by the particle swarm optimization algorithm to communication equipment troubleshooting, compared with the BP algorithm, it can speed up the training convergence while improving the error precision.