张浩晢, 杨智博, 焦绪国, 吕成兴, 雷鹏. 基于增强Bi-LSTM的船舶运动模型辨识[J]. 中国舰船研究. DOI: 10.19693/j.issn.1673-3185.03740
引用本文: 张浩晢, 杨智博, 焦绪国, 吕成兴, 雷鹏. 基于增强Bi-LSTM的船舶运动模型辨识[J]. 中国舰船研究. DOI: 10.19693/j.issn.1673-3185.03740
Ship motion model identification based on enhanced Bi-LSTM[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.03740
Citation: Ship motion model identification based on enhanced Bi-LSTM[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.03740

基于增强Bi-LSTM的船舶运动模型辨识

Ship motion model identification based on enhanced Bi-LSTM

  • 摘要: 【目的】针对基于数据驱动的船舶建模策略获得的模型预测精度低、适应性差等特点,提出一种增强双向长短期记忆网络(EBLSTM)用于船舶的高精度非参数化建模。【方法】首先利用双向长短期记忆网络(Bi-LSTM)的特点,实现对序列双向时间维度的特征提取。在此基础上,设计一维卷积神经网络提取序列的空间维度特征。最后,采用多头自注意力机制(MHSA)从多角度对序列进行自适应加权处理。利用KLVCC2船舶的航行数据,将EBLSTM模型与支持向量机(SVM)、门控循环单元(GRU)、长短期记忆网络(LSTM)模型的预测效果进行对比。【结果】EBLSTM模型在测试集中均方根误差(RMSE)、平均绝对误差(MAE)性能指标分别低于0.015、0.011,决定系数(R-squared)高于0.99913,预测精度显著高于

     

    Abstract: Objectives Aiming at the low prediction precision and poor adaptability of the ship model based on the data-driven modeling strategy, an enhanced bi-directional long short-term memory (EBLSTM) was proposed for high-precision non-parametric modeling of ships. Methods Firstly, the feature extraction of bidirectional time dimension is realized by using the feature of Bi-LSTM. On this basis, the spatial dimension features of the convolutional neural network extraction sequence were designed. Finally, multi-head self-attention (MHSA) mechanism is used to deal with the sequence from multiple angles. Using the navigation data of KLVCC2 ships, the prediction effects of EBLSTM

     

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