BP神经网络在弹丸落点预测中的应用

Application of BP Neural Network in Projectile Impact Point Prediction

针对传统弹丸落点预测方法存在的解算时间长、易产生累计误差等问题,提出了基于BP神经网络的预测方法,并且在标准BP算法的基础上使用Levenberg-Marquardt(LM)算法来改进网络参数的训练过程。利用大量的弹丸飞行状态参数和落点信息对构建的模型进行训练,得到用于预测弹丸落点的BP神经网络模型,并对其进行了仿真测试。仿真结果表明,上述方法能以较高的精度预测弹丸的落点,同时在解算时间上优于数值积分法。因此上述方法用于弹丸落点预测是合理可行的,为弹丸落点预测的实际应用提供了参考。

Aiming at the problems of long calculating time and cumulative error in traditional projectile impact point prediction method,a method based on BP neural network was proposed and Levenberg-Marquardt(LM)algorithm was used to improve the training process of network parameters on the basis of standard BP algorithm.The BP neural network model for predicting the impact point of projectile was obtained by training the model with a large number of flight state parameters and impact point information,and the simulation tests were carried out.The simulation results show that the method can predict the impact point with high accuracy,and it is superior to the numerical integration method in solving time.Therefore,it is reasonable and feasible to use this method to predict the impact point of projectile,which provides a reference for the practical application of projectile impact point prediction.