A CNN-SVM-based Adapter Drop Point Prediction Algorithm

doi:10.12382/bgxb.2024.0016

Abstract

Abstract:

To address the prolonged processing and resource consumption challenges in the launch process adapter drop point prediction algorithm,a adapter drop point prediction model with convolutional neural network and support vector machine (CNN-SVM) is proposed.The adapter dynamics and motion models are established by utilizing Euler angle representation,and the fourth-order Runge-Kutta method is used to numerically solve the motion trajectory of adapter to provide the extensive motion state parameters and drop point information.The CNN-SVM-based adapter drop point prediction model uses the Adam optimizer to optimize CNN network performance,and determines optimal SVM hyperparameters through mesh searching.Simulated results show that the proposed model has high solution accuracy and robust generalization performance for adapter drop prediction,achieving R² values exceeding 0.99 for both training and test sets and the mean absolute error (MAE) less than 0.1m.The solution time of the proposed method is only 8.5% compared to that of the traditional numerical integration method under the conditions of equivalent resources and the required prediction accuracy.The proposed model offers an efficient solution for rapidly predicting the adapter separation drop point during the launch process.

Key words: drop point prediction, adapter, convolutional neural network, support vector machine

CLC Number:

TJ760.9

SU Zhengyu, YANG Baosheng, YANG Jing, TANG Jingnan, JIANG Yi, DENG Yueguang. A CNN-SVM-based Adapter Drop Point Prediction Algorithm[J]. Acta Armamentarii, 2025, 46(2): 240016-.

Figures/Tables 22

Fig.1 Patriot missile launch process adapter separation[2]

Fig.2 CNN structure

Fig.3 Schematic diagram of SVM

Fig.4 Flowchart of CNN-SVM-based adapter drop point prediction

Fig.5 Schematic diagram of adapter separation durimgr launching

Fig.6 Illustration of coordinate system transformation

Fig.7 Adapter wind tunnel experiment under different angles of attack

Fig.8 Adapter separation experiment[25]

Fig.9 Comparison of experimental drop point distributions

Fig.10 Distribution density map of adapter drop points

Table 1 CNN model structural parameters

结构	卷积核	特征数	输出尺寸
输入层			7×1×1
卷积层1	2×1	32	6×1×32
池化层1	2×1	32	3×1×32
卷积层2	2×1	64	2×1×64
池化层2	2×1	64	1×1×64
全连接层			2×1×192

Fig.11 Predicted results of X-direction training set (R2=0.9968)

Fig.12 Predicted results of Y-direction training set (R2=0.9977)

Fig.13 Training set error analysis chart

Fig.14 Predicted results of X-direction training set (R2=0.9972)

Fig.15 Predicted results of Y-direction training set (R2=0.9973)

Fig.16 Test set error analysis chart

Table 2 Initial conditions for Monte Carlo experiment

参数	数值
初始X轴方向分离速度/(m·s^-1)	(3,3)
初始Y轴方向分离速度/(m·s^-1)	(-3,3)
初始Z轴方向分离速度/(m·s^-1)	(0,40)
初始分离角速度/(rad·s^-1)	(-15,15)
外部X轴方向风速/(m·s^-1)	(-10,10)
外部Y轴方向风速/(m·s^-1)	(-10,10)
发射过程随机振动/dB	(-5,5)

Fig.17 Result of Monte Carlo experiment

Fig.18 Box plot of Monte Carlo experiment

Fig.19 Solution speed comparison

Fig.20 Solution speed comparison

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