基于4种长短时记忆神经网络组合模型的畸形波预报

Journal of Shanghai Jiaotong University（2022）

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Abstract

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.

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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)

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