广东海洋大学学报2025，Vol.45Issue(2)：103-108,6.DOI:10.3969/j.issn.1673-9159.2025.02.013
基于PSO-RBFNN的船舶横摇运动实时预报
Real-time Prediction of Ship Roll Motion Based on PSO-RBFNN
摘要
Abstract
[Objective]To address the nonlinear and multi-variable coupling characteristics of ship roll motion,a real-time prediction model based on particle swarm optimization(PSO)and radial basis function neural network(RBFNN)was proposed to improve prediction accuracy and support intelligent navigation.[Method]This study developed a hybrid prediction scheme based on PSO and RBFNN.PSO was used to globally optimize the center and spread parameters of the RBFNN,and the PSO-RBFNN model was applied to predict ship roll motion.[Result]The feasibility and effectiveness of the proposed model were validated using measured and simulated data from the ship"Yukun".Simulation results demonstrated that PSO-RBFNN achieved excellent prediction performance[with mean absolute error(MAE)≤0.111 9,mean square error(MSE)≤0.028 0,root mean square error(RMSE)≤0.167 3,normalized root mean square error(NRMSE)≤0.021 2,mean absolute percentage error(MAPE)≤22.9%and coefficient of determination(R2)≥0.988 4 for a 3-second ahead forecast]under three different conditions,significantly outperforming models such as PSO-RNN,PSO-BP,and PSO-MLP.[Conclusion]The PSO-RBFNN model can efficiently and accurately predict ship roll motion,maintain stable performance under various operating conditions.It provides real-time and reliable technical support for intelligent navigation.关键词
船舶横摇运动/运动预报/智能航行/径向基函数神经网络/粒子群优化算法Key words
ship roll motion/motion prediction/intelligent navigation/radial basis function neural network/particle swarm optimization algorithm分类
交通工程引用本文复制引用
廖声浩,王立军,王思思,贾宝柱,尹建川,李荣辉..基于PSO-RBFNN的船舶横摇运动实时预报[J].广东海洋大学学报,2025,45(2):103-108,6.基金项目
国家自然科学基金(52171346,52271361) （52171346,52271361）
广东省南海海洋牧场智能装备重点实验室资助课题(2023B1212030003) （2023B1212030003）
广东省普通高校重点领域项目(2024ZDZX3054) （2024ZDZX3054）
