Research on the Correction Capability of High-spin and Tail-controlled Correction Projectile Based on HBBO-LSTM Network

doi:10.12382/bgxb.2024.0651

Abstract

Abstract:

To rapidly and accurately calculate the correction command of high-spin and tail-controlled correction projectiles,a correction capability prediction model based on hyperband algorithm-Bayesian optimization-long short-term memory (HBBO-LSTM) network is proposed for the prediction problem of correction capability.A 7DOF ballistic model of the high-spin and tail-controlled correction projectile is established.It is numerically simulated using the Runge-Kutta method to generate a large amount of sample data.By analyzing the dataset,a preprocessing method based on Ramanujan’s approximation formula is proposed to preprocess the original dataset for obtaining the sample data with uniform spatial distribution.A HBBO-LSTM network prediction model is constructed,and the optimal structural parameters are obtained through training.A learning rate decay strategy combining cosine annealing with restart mechanism and exponential decay is proposed to ensure the speed and stability of training process.The proposed model is compared with the long short-term memory network,gated recurrent unit network and back propagation network models on the same test set through simulation.It is also evaluated against the numerical integration method for 4DOF correction projectile ballistic equation.The results show that the prediction accuracy of the HBBO-LSTM network model is superior to those of other models with an overall mean squared error of 0.17m² and an overall mean absolute error of 0.33m.Additionally,the HBBO-LSTM model outperforms the numerical integration method in both computation time and prediction accuracy.This demonstrates that the HBBO-LSTM network model has high feasibility and reference value.

Key words: correction capability, ballistic correction projectile, tail-controlled projectile, long short-term memory network, hyperband algorithm, Bayesian optimization

CLC Number:

TJ012.3

ZHOU Jie, WANG Liangming, FU Jian, WANG Yanqin, GUO Shouyu. Research on the Correction Capability of High-spin and Tail-controlled Correction Projectile Based on HBBO-LSTM Network[J]. Acta Armamentarii, 2025, 46(7): 240651-.

Figures/Tables 15

References 25

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特征名	最小值	最大值	步长
射角/(°)	45	51	1
起控时刻/s	10	89	1
滚转角/(°)	0	359.5	0.5

特征名	最小值	最大值	步长
射角/(°)	45	51	1
起控时刻/s	10	89	1
滚转角/(°)	0	359.5	0.5

超参数名称	范围	步长
高斯噪层的标准差	[0,0.003]	0.0002
LSTM层1神经元数量	[100,2000]	20
LSTM层1正则化系数	[0,0.002]	0.0002
Dropout层1丢弃率	[0,0.5]	0.1
LSTM层2神经元数量	[100,1500]	20
LSTM层2正则化系数	[0,0.002]	0.0002
Dropout层2丢弃率	[0,0.5]	0.1
初始全局学习率	[0.0001,0.01]	对数采样
批量大小	[32,256]	32

超参数名称	范围	步长
高斯噪层的标准差	[0,0.003]	0.0002
LSTM层1神经元数量	[100,2000]	20
LSTM层1正则化系数	[0,0.002]	0.0002
Dropout层1丢弃率	[0,0.5]	0.1
LSTM层2神经元数量	[100,1500]	20
LSTM层2正则化系数	[0,0.002]	0.0002
Dropout层2丢弃率	[0,0.5]	0.1
初始全局学习率	[0.0001,0.01]	对数采样
批量大小	[32,256]	32

层类型	层参数	层输出形状
高斯噪声层	标准差0.0002	(64,1,7)
LSTM层1	神经元数量1500	(64,1,1500)
激活层1	激活函数Leaky ReLU	(64,1,1500)
LSTM层2	神经元数量740	(64,740)
激活层2	激活函数Leaky ReLU	(64,740)
全连接层	神经元数量2	(64,2)