摘要
为提高BP神经网络诊断发动机气路故障的准确率,利用遗传算法对BP神经网络的初始连接权值和阀值在解空间内进化寻优,再将优化结果赋给网络以梯度下降算法进行二次训练,再对待检故障样本进行诊断。结果表明:GA-BP网络在输出精度、收敛速度及收敛曲线平滑性上明显优于普通BP网络,为航空发动机故障诊断领域的研究提出了新的思路和方法,具有一定研究价值。
In order to improve the accuracy rate of aero-engine gas-path fault diagnosis based on BP neural network,this research uses the genetic algorithm to optimize the initial weights and thresholds of BP neural network in their solution space,retrains the results by gradient descent algorithm and uses the optimized network to testify the fault samples. The result shows that GA-BP network has a higher precision and converges faster,and its convergence curve is smoother than that of the common BP network. This work can put forward new ideas and methods for aero-engine fault diagnosis and has a certain research value.
出处
《机床与液压》
北大核心
2015年第18期31-36,共6页
Machine Tool & Hydraulics
基金
supported by The 4th Boeing Technical Challenge Fund (201410059)
