摘要
以某俄式发动机为研究对象,根据该发动机故障分布,使用优化后的BP神经网络对该故障率模拟;针对BP神经网络可能陷入局部极小值点的问题,在激励函数中加入模糊参数;与原网络相比,在不同学习速率的条件下,优化后的BP神经网络预测结果与实际故障率更为拟合,预测结果更为准确;该模型对于该型号发动机的故障预测具有一定参考意义。
In this paper,a Russian engine is taken as the research object.According to the fault distribution of the engine,the optimized BP neural network is used to simulate the failure rate.Aiming at the problem that BP neural network may fall into local minimum point,fuzzy parameters are added into the excitation function.Compared with the original network,under the condition of different learning rates,the optimized BP neural network prediction results are more fitting with the actual failure rate,and the prediction results are more accurate.This model has some reference significance for the fault prediction of this type of engine.
作者
薛永亮
陈振林
Xue Yongliang;Chen Zhenlin(Navy Aviation University Coast Dedence College,Yantai264001,China)
出处
《计算机测量与控制》
2019年第5期44-47,共4页
Computer Measurement &Control
关键词
故障率
BP神经网络
预测
failure rate
BP neural network
prediction