基于GA-BP的移动通信设备故障诊断

针对通信设备故障发生随机性强,影响因素多,对应的故障诊断有高度非线性和不确定性的特点,采用BP神经网络算法,优化的GA-BP神经网络算法和POS-BP神经网络算法分别搭建基站设备故障诊断模型,提取设备故障历史数据进行MATLAB仿真,准确预测设备故障类型,帮助提高代维公司调度管理的智能化水平,提高基站设备运维的执行效率。仿真结果表明：本文的BP,GA-BP和POS-BP神经网络算法都能够实现设备故障类别的预测,且GA-BP神经网络算法相比BP和POS-BP神经网络算法对通信设备故障诊断有更好的适应性。

Fault Diagnosis of Mobile Communication Equipment Based on GA-BP

The neural network has good self-learning ability, powerful parallel processing capabilities and advantages that can approximate any nonlinear function. For communication equipment malfunction occurrence is random, can be affected by many factors, corresponding Troubleshooting Has a highly nonlinear and uncertainty characteristics. BP neural network algorithm, optimized GA-BP neural network algorithm and POS-BP neural network algorithm are used to Build a base station equipment fault diagnosis model Respectively, the base station equipment malfunction historical data are extracted for simulation, predicting equipment malfunction type accurately, To help raise the level of intelligence of dispatching management for Maintenance Company, improving the efficiency of the operation and maintenance of base station equipment. The Simulation results show that: BP paper, GA-BP and POS-BP neural network algorithms are able to achieve the goals of predicted the category of equipment malfunction, and the GA-BP neural network algorithm compared to BP or POS-BP neural network algorithm for communications equipment troubleshooting has better adaptability.