Year: 2025
Type: article
Abstract: Accurate prediction of ship motion attitude remains a significant challenge due to the inherent non-stationarity and strong stochasticity of marine environmental conditions. To address this issue, this study proposes FMD-IBKA-BTGN, a hybrid model combining Feature Mode Decomposition (FMD), Improved Black-winged Kite Algorithm (IBKA), and a Bidirectional Temporal Convolutional Network with Gated Recurrent Unit (BTGN). First, FMD decomposes motion signals into intrinsic modes. Subsequently, IBKA-enhanced with chaotic mapping and Lévy flights-optimizes BTGN hyperparameters for global search efficiency. Finally, predictions from all components are ensembled for final output. Experiments on a 240 m vessel in Sea State 4 show our model outperforms six models, reducing MAPE by 20.38%, RMSE by 7.4%, MAE by 4.2%, and MSE by 0.97% versus LSTM. The model enhances both prediction accuracy and generalization.
Source: Sensors
DOI: 10.3390/s25216602
Authors: Chunyuan Shi, Yanguan Su, Biao Zhang
Institution: Harbin University of Science and Technology
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415650215