Zheng Yuanzhou, Li Lei, Qian Long, Cheng Bosheng, Hou Wenbo, Zhuang Yuan
School of Navigation, Wuhan University of Technology, Wuhan 430036, China.
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430036, China.
Sensors (Basel). 2023 Jan 8;23(2):704. doi: 10.3390/s23020704.
In this paper, we propose a Sine chaos mapping-based improved sparrow search algorithm (SSA) to optimize the BP neural network for trajectory prediction of inland river vessels because of the problems of poor accuracy and easy trapping in local optimum in BP neural networks.
First, a standard BP model is constructed based on the AIS data of ships in the Yangtze River section. A Sine-BP model is built using Sine chaos mapping to assign neural network weights and thresholds. Finally, a Sine-SSA-BP model is built using the sparrow search algorithm (SSA) to solve the optimal solutions of the neural network weights and thresholds.
The Sine-SSA-BP model effectively improves the initialized population of uniform distribution, and reduces the problem that population intelligence algorithms tend to be premature.
The test results show that the Sine-SSA-BP neural network has higher prediction accuracy and better stability than conventional LSTM and SVM, especially in the prediction of corners, which is in good agreement with the real ship navigation trajectory.
由于BP神经网络存在精度差和易陷入局部最优的问题，本文提出一种基于正弦混沌映射的改进麻雀搜索算法（SSA）来优化BP神经网络，用于内河船舶轨迹预测。
首先，基于长江段船舶的AIS数据构建标准BP模型。利用正弦混沌映射分配神经网络权重和阈值，构建正弦-BP模型。最后，使用麻雀搜索算法（SSA）构建正弦-SSA-BP模型来求解神经网络权重和阈值的最优解。
正弦-SSA-BP模型有效改善了初始种群的均匀分布，减少了群体智能算法易早熟的问题。
测试结果表明，正弦-SSA-BP神经网络比传统的LSTM和SVM具有更高的预测精度和更好的稳定性，尤其是在拐角预测方面，与实际船舶航行轨迹吻合良好。
