文献类型：期刊文献

中文题名：Real-Time Ship Roll Prediction via a Novel Stochastic Trainer-Based Feedforward Neural Network

英文题名：Real-Time Ship Roll Prediction via a Novel Stochastic Trainer-Based Feedforward Neural Network

作者：Xu, Dong-xing[1,2,3];Yin, Jian-chuan[1,2,3]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524005, Peoples R China;[2]Guangdong Prov Engn Res Ctr Ship Intelligence & Sa, Zhanjiang 524005, Peoples R China;[3]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2025

卷号：39

期号：4

起止页码：608

中文期刊名：China Ocean Engineering

外文期刊名：CHINA OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001572420800010)、、EI(收录号：20253819207523)、Scopus(收录号：2-s2.0-105016459564)、WOS

基金：This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 52231014 and 52271361) and the Natural Science Foundation of Guangdong Province of China (Grant No. 2023A1515010684).

语种：英文

中文关键词：ship roll prediction;data preprocessing strategy;sliding data widow;improved black widow optimization algorithm;stochastic trainer;feedforward neural network

外文关键词：ship roll prediction; data preprocessing strategy; sliding data widow; improved black widow optimization algorithm; stochastic trainer; feedforward neural network

中文摘要：Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.

外文摘要：Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency. To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics, a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network. The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability. The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series. The energy entropy method reconstructs the mode components into high-frequency, medium-frequency, and low-frequency series to reduce model complexity. An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component, enabling accurate tracking of abrupt signals. Additionally, the deterministic algorithm trainer-based neural network, characterized by rapid processing speed, predicts the remaining two mode components. Thus, real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results. The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial. The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.