[1]ZHANG Yanfeng,YANG Zhen,WANG Lipeng,et al.Multi-modal prediction method for ship motion based on LSTM[J].CAAI Transactions on Intelligent Systems,2026,21(1):201-213.[doi:10.11992/tis.202512005]
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Multi-modal prediction method for ship motion based on LSTM
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
21
Number of periods:
2026 1
Page number:
201-213
Column:
学术论文—水下智能与海洋具身智能
Public date:
2026-03-05
- Title:
- Multi-modal prediction method for ship motion based on LSTM
- Keywords:
- ship; roll; pitch; time series; short-term prediction; long short-term memory; double-layer modal decomposition; Kepler optimization
- CLC:
- TP183
- DOI:
- 10.11992/tis.202512005
- Abstract:
- To improve the prediction accuracy of the long short-term memory (LSTM) model in the field of ship motion attitude and simplify the parameter tuning process, a double-layer mode decomposition strategy is designed for ship motion data. Combined with the Kepler optimization algorithm (KOA), a multi-modal prediction model for ship motion attitude is proposed. This model adopts a double-layer mode decomposition (DLMD) scheme integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) to perform double-layer decomposition on the original time-series data of ship motion attitude. This decomposition resolves the over-smoothing issue of high-frequency components in the first-layer decomposition, while eliminating the coupling interference between high-frequency noise and effective signals in the same frequency band, thus improving the prediction performance of the model. Furthermore, KOA is introduced to optimize the hyperparameters of LSTM during model training, which addresses the problems of low efficiency and easy trapping in local optima in LSTM parameter tuning. Ablation experiments and overall algorithm verification experiments are conducted based on a real ship motion dataset. The ablation experiments verify the independent contributions of the DLMD and KOA modules; the results of the algorithm verification experiments confirm the synergistic effect of the two modules, and demonstrate that the proposed model can achieve high-precision prediction of ship roll and pitch attitudes as well as automatic optimization of hyperparameter combinations.
- References:
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Last Update:
2026-01-05