Application of Improved LSTM Neural Network in Time-Series Prediction of Extreme Short-Term Wave

被引：0

作者：

Shang F. ^{[1
]}

Li C. ^{[1
,2
]}

Zhan K. ^{[1
]}

Zhu R. ^{[1
]}

机构：

[1] State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai

[2] State Key Laboratory of Navigation and Safety Technology, Shanghai Ship and Shipping Research Institute, Shanghai

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2023年 / 57卷 / 06期

关键词：

extreme short-term prediction; generative adversarial; long short-term memory (LSTM) neural network; time-series analysis;

D O I：

10.16183/j.cnki.jsjtu.2021.438

中图分类号：

学科分类号：

摘要：

Efficient and accurate extreme short-term prediction is of great significance for the safety of ship and marine structures in actual sea waves. Due to the stochastic of actual sea waves, short-term prediction always uses time series analysis. The neural networks, particularly long short-term memory (LSTM) neural networks, have received increasing attention for their powerful forecasting capability in time series analysis. Based on this, an improved form of LSTM combining generative adversarial ideas is proposed, in which the frequency domain characteristics are embedded in the neural network to achieve coupled time-frequency domain information forecasting. The experimental test shows that the forecasting accuracy of this method is better than the results of traditional time series analysis methods and the LSTM neural network, and it is suitable for extreme short-term time series prediction for better ship maneuvering. © 2023 Shanghai Jiao Tong University. All rights reserved.

引用

页码：659 / 665

页数：6

共 15 条

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