基于改进的GA优化BP神经网络的煤矿设备的故障诊断

Fault Diagnosis of Coal Mine Equipment Based on Improved GA Optimization BP Neutral Network

面对越来越多的煤矿设备出现的故障,本文提出了将遗传算法(GA)和BP神经网络结合进行预测的方法。针对遗传算法存在收敛速度慢,容易陷入局部最优的情况,本文首先采用混沌和反向学习初始化个体,其次运用差分算法对个体最优进行操作,最后,将改进的适应度函数运用到选择操作中,通过变异概率和交叉概率提高操作的准确率。将改进后的算法运用到BP神经网络中提高了样本训练效果。

In the face of more and more coal mine equipment failure, this paper proposes a method of combining genetic algorithm（GA） and BP neural network. For genetic algorithm, the convergence speed is slow and easy to fall into local optimum. In this paper, we first use chaos and backward learning to initialize the individual. Secondly, we use the differential algorithm to operate the individual. Finally, the improved fitness function is applied to the selection operation. The improved algorithm is applied to the BP neural network to improve the training effect. The simulation results show that the proposed algorithm can improve the accuracy and stability compared with the traditional BP neural network.