Abstract Aiming at the problem that the traditional algorithm has large prediction error on motion trajectory and short prediction distance, this paper proposes a GA-Elman-Regularization based neural network method. The GA algorithm has the characteristics of parallel search global optimal solution, which makes up for the shortcomings of static property given by neural network model and the tendency of training algorithm to fall into partial optimal solution, and introduces regularization terms to improve the generalization ability of the network, also improves the prediction accuracy of the network. Comparison of experimental results of motion trajectory prediction by Elman neural network, GA-Elman neural network and GA- Elman-Regularization neural network on semi-physical dataset, the predicted average errors are 1.37%, 0.82% and 0.556%. Experiments show that the optimized algorithm improved the generalization ability of the network and the accuracy of prediction.

中文翻译：

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基于GA-Elman-Regularization神经网络的飞机运动轨迹多点预测

摘要 针对传统算法运动轨迹预测误差大、预测距离短的问题，提出一种基于GA-Elman-Regularization的神经网络方法。GA算法具有并行搜索全局最优解的特点，弥补了神经网络模型给出的静态特性和训练算法容易陷入局部最优解的缺点，并引入了正则化项来提高泛化能力。网络，也提高了网络的预测精度。比较Elman神经网络、GA-Elman神经网络和GA-Elman-Regularization神经网络在半物理数据集上的运动轨迹预测实验结果，预测的平均误差分别为1.37%、0.82%和0.556%。