基于LSTM-RNN的船舶操纵运动黑箱建模  

LSTM-RNN based black-box modeling of ship manoeuvring motion

作　　者：田延飞 李知临 艾万政 韩喜红 TIAN Yanfei;LI Zhilin;AI Wanzheng;HAN Xihong(School of Naval Architecture and Maritime,Zhejiang Ocean University,Zhoushan 316022,China)

机构地区：[1]浙江海洋大学船舶与海运学院,浙江舟山316022

出　　处：《舰船科学技术》2024年第11期80-84,共5页Ship Science and Technology

基　　金：浙江省教育厅资助项目(Y202147772)。

摘　　要：当无需揭示船舶操纵运动机理过程,而只需对输入输出建立映射时,黑箱建模成为一种有效途径。本文基于长短期记忆-循环神经网络(Long Short-term Memory-recurrent Neural Network,LSTM-RNN)构建船舶航向-舵角黑箱模型,LSTM网络为10-10-1结构,误差指标为RMSE,参数学习采用Adam算法。开展实船Z型操纵实验获取了航向-舵角数据。前70%用于模型训练,后30%用于模型测试。训练后的模型使得RMSE达到设计目标。对测试集数据,训练后模型拟合优度在0.98以上,表明其具有良好的有效性和泛化性。文中航向-舵角LSTM-RNN黑箱模型结构简明清晰,参数明确,易于实际操作使用,为航向-舵角关系建模提供了一种可行方法。Black box modeling becomes an effective approach when it is not necessary to reveal the mechanism of ship maneuvering motion,but only to establish a mapping of input and output.The paper focuses on building a black model of heading angle-rudder angle based on LSTM-RNN.The LSTM layer has a structure of 10-10-1 nodes.Error indicator for measuring the degree of fit is RMSE.The Adam algorithm is adopted for parameter learning.Zigzag manoeuvering experiments by using a full-scale ship maneuvering experiments are conducted to obtain Input and output data,namely the heading angle-rudder angle.The first 70%of the data is used for model training,while the last 30%is used for model testing.The trained model enables RMSE to achieve the hoped-for goals.For the testing data,the goodness of fit of the trained model is above 0.98,indicating that the final LSTM-RNN model has good effectiveness and generalization.The LSTM-RNN black box model for heading angle-rudder angle has a concise and clear structure,clear parameters,and is easy to use in practical operations.It provides a feasible method for modeling the relationship between heading angle and rudder angle for full scale ship.

关 键 词：船舶操纵运动 黑箱建模 机器学习 LSTM-RNN 

分 类 号：U661.33[交通运输工程—船舶及航道工程]