基于4种长短时记忆神经网络组合模型的畸形波预报

doi:10.16183/j.cnki.jsjtu.2021.088

摘要/Abstract

摘要：

为提高长短时记忆神经网络对畸形波预报精度,研究了长短时记忆神经网络与卷积神经网络(Convolution Neural Networks, CNN)、经验模式分解(Empirical Mode Decomposition, EMD)、差分自回归移动(Auto-Aggressive Integrated Moving Average, ARIMA)模型以及卡尔曼滤波 (Kalman Filtering,KF)方法4种组合模型预报方法.基于两个单峰型畸形波和一个三姐妹组合型畸形波实验数据,经过数据归一化、模型参数设置及误差评估建立了组合预报模型和预报.结果表明:4种组合模型预报精度在所研究的3个畸形波序列预报中精度都得到了显著提高,其中与CNN组合模型的预报精度最高.组合模型方法为提高畸形波预报精度提供了可行方案.

关键词: 畸形波, 长短时记忆(LSTM), 卷积神经网络(CNN), 经验模式分解(EMD), 差分自回归(ARIMA), 卡尔曼滤波(KF)

Abstract:

In order to improve the prediction accuracy of rogue waves of the long short-term memory (LSTM) neural network, prediction methods of LSTM with convolution neural networks (CNN), empirical mode decomposition (EMD), auto-aggressive integrated moving averagel (ARIMA) model, and Kalman filtering (KF) were studied. Based on the experimental data of the rogue waves of two single-peak and one three combined peaks, prediction models were established and predicted by data normalization, model parameter optimization and error evaluation. The results show that the prediction accuracy of the four combined models is significantly improved in all the three studied conditions, and the combination with the convolutional neural network has the highest prediction accuracy. The combined models provide a feasible scheme for improving the prediction accuracy of freak waves.

Key words: rogue wave, long short-term memory (LSTM), convolutional neural network (CNN), empirical mode decomposition (EMD), auto-aggressive integrated average (ARIMA), Kalman filtering (KF)

中图分类号:

U661.1

赵勇, 苏丹. 基于4种长短时记忆神经网络组合模型的畸形波预报[J]. 上海交通大学学报, 2022, 56(4): 516-522.

ZHAO Yong, SU Dan. Rogue Wave Prediction Based on Four Combined Long Short-Term Memory Neural Network Models[J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 516-522.

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工况	有义波高/cm	最大波高/cm	波高比	峰值时间/s	峰值/cm
1	4.59	9.63	2.11	77.34	5.75
2	2.89	6.33	2.19	45.96	3.69
3	2.89	6.36	2.20	53.04	3.98

参数类别	模型	数量设置
隐藏层层数	LSTM	1
	LSTM-CNN	3
	LSTM-EMD	2
	LSTM-ARIMA	1
	LSTM-Kalman	1
隐藏层节点数	LSTM	128
	LSTM-CNN	300
	LSTM-EMD	20
	LSTM-ARIMA	3
	LSTM-Kalman	4
批尺寸	LSTM	32
	LSTM-CNN	16
	LSTM-EMD	20
	LSTM-ARIMA	1
	LSTM-Kalman	1
训练次数	LSTM	100
	LSTM-CNN	1000
	LSTM-EMD	100
	LSTM-ARIMA	100
	LSTM-Kalman	100

工况	e_m
工况	LSTM	LSTM- CNN	LSTM- ARIMA	LSTM- EMD	LSTM- Kalman
1	0.2838	0.1557	0.1621	0.1987	0.2047
2	1.0637	0.2685	0.3240	0.5965	0.6789
3	0.2664	0.1307	0.1413	0.2079	0.2366