文献类型：期刊文献

英文题名：An adaptive real-time ship roll motion prediction scheme based on two-stage multi-resolution decomposition

作者：Yin, Jianchuan[1,2];Wang, Nini[3];Shu, Yaqing[4]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524009, Peoples R China;[2]Guangdong Prov Engn Res Ctr Ship Intelligence & Sa, Zhanjiang 524088, Peoples R China;[3]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang 524009, Peoples R China;[4]Liverpool John Moores Univ, Liverpool Logist Offshore & Marine LOOM Res Inst, Liverpool, England

年份：2025

卷号：325

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001436943700001)、、EI(收录号：20250917970205)、Scopus(收录号：2-s2.0-85218870679)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684, and the Program for Scientific Research Start-Up Funds of Guangdong Ocean University, and the European Research Council project under the European Union's Horizon 2020 research and innovation program (TRUST CoG 2019 864724) .

语种：英文

外文关键词：Ship roll prediction; Empirical mode decomposition; Discrete wavelet decomposition; Lipschitz quotients; Variable neural network

外文摘要：Real-time prediction of ship roll motion is crucial for enhancing marine safety and efficiency. To address the complex characteristics of ship roll dynamics, including nonlinearity, time-varying dynamics, and uncertainty induced by environmental disturbances and sailing conditions, an adaptive real-time ship roll neural prediction scheme is proposed based on a two-stage decomposition framework integrating empirical mode decomposition (EMD) and discrete wavelet transformation (DWT). The multi-resolution decomposition capabilities of EMD and DWT are combined with variable neural networks to achieve robust prediction performance. The decomposition order and the prediction model input order are adaptively determined based on EMD and Lipschitz quotients methods, respectively. The adaptability of the neural prediction scheme is enhanced with the network dimension, hidden units' locations, and connecting parameters being real-time adjusted in a sequential learning mode. The two-stage EMD-DWT transformation and the parallel neural prediction strategies ensure the accuracy and stability of the prediction, and the sequential learning strategy of sliding data window enables fast processing speed and adaptability to time-varying dynamics. The feasibility and effectiveness of the proposed ship roll prediction scheme are validated through simulations based on the measured data of the real ship trial.