Sine-SSA-BP Ship Trajectory Prediction Based on Chaotic Mapping Improved Sparrow Search Algorithm

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 2; p. 704
• Main Authors: Zheng, Yuanzhou, Li, Lei, Qian, Long, Cheng, Bosheng, Hou, Wenbo, Zhuang, Yuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.01.2023; MDPI
• Subjects: Accuracy; Algorithms; Back propagation; Back propagation networks; BP neural network; Identification systems; Kalman filters; Mapping; Neural networks; Propagation; Search algorithms; ship trajectory prediction; sine chaos mapping; sparrow search algorithm; System theory; Thresholds; Time series; Transportation services; sine chaos mapping; BP neural network; ship trajectory prediction; sparrow search algorithm
• ISSN: 1424-8220; 1424-8220
• 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.