中文摘要：

为更有效地实现具有复杂性、时变性及非线性的机场滑行道安全风险预警,降低事故发生率,针对小波神经网络（WNN）训练过程易陷入局部最优以及训练不稳定等影响预测准确性问题,采用蝙蝠算法（BA）优化WNN,设计和实现基于BA-WNN的滑行道安全风险预警方法,并将其与BP神经网络（BPNN）、WNN、遗传算法优化小波网络（GA-WNN）等3种方法进行有效性对比。结果表明：BA-WNN方法的预警准确率最高（约为84%）,在所有工况下误警率都较低。

英文摘要：

For the sake of finding a more effective solution to safety risk early warning of taxiway in the airport,WNN was chosen as the main method for realizing the safety risk early warning of taxiway.Seeing that the training process of WNN is easy to fall into local optimum and the training is unstable,BA was used to optimize WNN.A BA-WNN based safety risk early warning method of taxiway in the airport was worked out.An effectiveness comparison was made between BPNN,WNN and GA-WNN and BA-WNN method.The results show that BA-WNN has the highest accuracy rate of 84%,and a low false alarm rate under all working conditions.