SA-BP神经网络在轨道电路故障诊断中的应用研究

针对25 Hz相敏轨道电路故障的复杂性,提出一种模拟退火(SA,Simulated Annealing)算法与BP神经网络相结合的故障诊断方法。发挥SA算法全局寻优的特点来优化BP神经网络的学习过程,避免网络训练时间长和陷入局部极小值;通过Matlab进行仿真分析,结果表明,将该方法应用于轨道电路故障诊断,可有效提高故障诊断效率和准确度。

SA-BP neural network applied to track circuit fault diagnosis

Aiming at the complexity of 25 Hz phase sensitive track circuit fault, this paper proposed a fault diagnosis method combining simulated annealing (SA) algorithm with BP neural network.The learning process of BP neural network was optimized by using the global optimization feature of SA algorithm to avoid long training time and falling into local minimum.Through Matlab simulation analysis, the results show that the application of this method to track circuit fault diagnosis can effictively improve the efficiency and accuracy of fault diagnosis.