[1]ZHANG Haozhe,YANG Zhibo,JIAO Xuguo,et al.Identification of ship motion model based on self-optimizing neural network[J].CAAI Transactions on Intelligent Systems,2025,20(3):571-583.[doi:10.11992/tis.202408004]
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Identification of ship motion model based on self-optimizing neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 571-583 Column: 学术论文—机器学习 Public date: 2025-05-05
Title:
Identification of ship motion model based on self-optimizing neural network
Author(s):
ZHANG Haozhe1;  YANG Zhibo1;  JIAO Xuguo1;  LYU Chengxing1;  ZHU Qidan2
1. School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China;
2. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
ship motion modeling; improved snow ablation optimizer; bidirectional temporal convolutional network; attention mechanism; optimize; hyperparameter; predict; identification
CLC:
TP183; U661.3
DOI:
10.11992/tis.202408004
Abstract:
An accurate ship motion model stands as the cornerstone of autonomous ship systems. To enhance the precision of ship motion modeling, an improved snow ablation optimizer (ISAO) is first introduced. Subsequently, a network model, BITCA, which integrates a bidirectional temporal convolutional network (Bi-TCN) with the attention mechanism (AM), is proposed. Furthermore, by combining the ISAO with BITCA, a hybrid identification model for ship motion, termed ISAO-BITCA, is established. This model initially leverages the Bi-TCN to deeply explore the hidden features of ship motion sequences across both temporal and spatial dimensions, while introducing the AM to mitigate information loss. Utilizing the ISAO, the hyperparameter combination for the BITCA model is autonomously searched and optimized. Simulation results demonstrate that the BITCA model optimized by the ISAO achieves reductions in the root mean square error for ship heading angle, yaw rate, roll angle, and total speed predictions by 54.1%, 28.21%, 5.88%, and 40%, respectively, providing an effective means for the accurate identification of ship motion models.
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