基于卷积神经网络与支持向量机的适配器落点预测方法

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. 1 m. 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.