上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 8: 963 - 971

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.089

船舶海洋与建筑工程

一种CNN-LSTM船舶运动极值预报模型

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  • 1.上海交通大学 船舶海洋与建筑工程学院
    2.海洋工程国家重点实验室,上海 200240
詹 可(1997-),博士生,从事船海工程水动力学研究.

收稿日期: 2022-03-31

  修回日期: 2022-05-27

  录用日期: 2022-07-27

  网络出版日期: 2022-10-27

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A CNN-LSTM Ship Motion Extreme Value Prediction Model

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  • 1. State Key Laboratory of Ocean Engineering
    2. School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2022-03-31

  Revised date: 2022-05-27

  Accepted date: 2022-07-27

  Online published: 2022-10-27

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摘要

针对船舶摇荡运动的短期极值预报,提出一种融合运动频谱信息的滑动窗口方法提取特征数据,在此基础上构建卷积神经网络(CNN)与长短时记忆(LSTM)网络的组合预报模型,其中CNN模块针对输入数据的局部相关特征,LSTM模块针对数据的时间维度特征.对S175船进行运动仿真测试,结果表明,该模型对未来1~2个周期内的运动极值信息预报效果良好,各项评价指标均明显优于LSTM和门控循环单元(GRU)模型,具有重要的应用价值.

关键词： 局部极值; 短期预报; 卷积神经网络; 长短时记忆网络

本文引用格式

詹可, 朱仁传 . 一种CNN-LSTM船舶运动极值预报模型[J]. 上海交通大学学报, 2023 , 57(8) : 963 -971 . DOI: 10.16183/j.cnki.jsjtu.2022.089

Abstract

Aimed at the short-term extreme value prediction of ship motion, a sliding window method based on motion spectrum information is proposed to extract feature data, based on which, a series prediction model of convolutional neural networks (CNN) and long short-term memory (LSTM) is built. The CNN module aims at the local correlation characteristics of the input data, and the LSTM module aims at the time dimension characteristics of the data. The simulation test results of S175 ship show that the model has a good prediction effect on the motion extremum information in the next 1 and 2 cycles, and the evaluation indexes are significantly better than those of LSTM and gate recurrent unit (GRU) models, which has an important application value.

Key words： local extremum; short-term prediction; convolutional neural network (CNN); long short-term memory (LSTM) networks

参考文献

[1] 喻元根, 马雪泉, 季盛. 基于AR的船舶运动极短期预报摇摆平台试验[J]. 上海船舶运输科学研究所学报, 2016, 39(4): 4-7.
[1] YU Yuangen, MA Xuequan, JI Sheng. Short time prediction of ship motion based on AR model and stewart platform experiment[J]. Journal of Shanghai Ship and Shipping Research Institute, 2016, 39(4): 4-7.
[2] 范海平. 基于卡尔曼滤波技术的船舶横摇预测方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2008.
[2] FAN Haiping. Study on the prediction and estimation of ship roll motion based on Kalman filter[D]. Harbin: Harbin Engineering University, 2008.
[3] ZHOU B, SHI A. Empirical mode decomposition based LSSVM for ship motion prediction[C]//International Symposium on Neural Networks. Berlin, Heidelberg, Germany: Springer, 2013.
[4] PENA F L, GONZALEZ M M, CASAS V D, et al. An ANN based system for forecasting ship roll motion[C]//Computational Intelligence and Virtual Environments for Measurement Systems and Applications. Milan, Italy: IEEE Instrumentation & Measurement Magazine, 2013.
[5] HOCHREITER S, SCHMIDHUBER R A, et al. Long short-term memory[J]. Neural Computation, 1997, 9: 1735-1780.
[6] WANG Y, WANG H, ZHOU B, et al. Multi-dimensional prediction method based on Bi-LSTMC for ship roll[J]. Ocean Engineering, 2021, 242: 110106.
[7] WANG Y, WANG H, ZOU D, et al. Ship roll prediction algorithm based on Bi-LSTM-TPA combined model[J]. Journal of Marine Science and Engineering, 2021, 9(4): 387-1-16.
[8] ZHANG T, ZHENG X Q, LIU M X. Multiscale attention-based LSTM for ship motion prediction[J]. Ocean Engineering, 2021, 230(13): 109066.
[9] 张彪, 彭秀艳, 高杰. 基于ELM-EMD-LSTM组合模型的船舶运动姿态预测[J]. 船舶力学, 2020, 24(11): 1413-1421.
[9] ZHANG Biao, PENG Xiuyan, GAO Jie. Ship motion attitude prediction based on ELM-EMD-LSTM integrated model[J]. Journal of Ship Mechanics, 2020, 24(11): 1413-1421.
[10] STOICA P, MOSES R. Spectral analysis of signals[M]. New Jersey, USA: Prentice Hall, 2005.
[11] KETKAR N, MOOLAYIL J. Deep Learning with Python[M]. Berkeley, USA: Apress, 2021: 197-242.
[12] 赵建利, 白格平, 李英俊, 等. 基于CNN-LSTM的短期风电功率预测[J]. 自动化仪表, 2020, 41(5): 37-41.
[12] ZHAO Jianli, BAI Geping, LI Yingjun, et al. Short-term wind power prediction based on CNN-LSTM[J]. Process Automation Instrumentation, 2020, 41(5): 37-41.
[13] 王国栋. 基于LSTM的舰船运动姿态短期预测及仿真研究[D]. 镇江: 江苏科技大学, 2017.
[13] WANG Guodong. Short-term prediction and simulation of ship's motion based on LSTM[D]. Zhenjiang: Jiangsu University of Science and Technology, 2017.
[14] 刘秀丽, 徐小力. 基于特征金字塔卷积循环神经网络的故障诊断方法[J]. 上海交通大学学报, 2022, 56(2): 182-190.
[14] LIU Xiuli, XU Xiaoli. Fault diagnosis method based on feature pyramid CRNN network[J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 182-190.
[15] TRISTAN P, FOSSEN T I. A MATLAB toolbox for parametric identification of radiation-force models of ships and offshore structures[J]. Modeling, Identification and Control, 2009, 30(1): 1-15.
[16] 朱仁传, 缪国平. 船舶在波浪上的运动理论[M]. 上海: 上海交通大学出版社, 2019: 185-186.
[16] ZHU Renchuan, MIAO Guoping. The theory of ship motion in waves[M]. Shanghai: Shanghai Jiao Tong University Press, 2019: 185-186.
[17] MWL A, DYX A, JING G A, et al. A hybrid approach for forecasting ship motion using CNN-GRU-AM and GCWOA[J]. Applied Soft Computing, 2022, 114: 108084.
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