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嵌入多阶泰勒微分知识的多尺度注意力循环网络深度时空序列预测方法

孙强 赵珂

孙强, 赵珂. 嵌入多阶泰勒微分知识的多尺度注意力循环网络深度时空序列预测方法[J]. 电子与信息学报. doi: 10.11999/JEIT231108
引用本文: 孙强, 赵珂. 嵌入多阶泰勒微分知识的多尺度注意力循环网络深度时空序列预测方法[J]. 电子与信息学报. doi: 10.11999/JEIT231108
SUN Qiang, ZHAO Ke. Multi-scale Attention Recurrent Network with Multi-order Taylor Differential Knowledge for Deep Spatiotemporal Sequence Prediction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231108
Citation: SUN Qiang, ZHAO Ke. Multi-scale Attention Recurrent Network with Multi-order Taylor Differential Knowledge for Deep Spatiotemporal Sequence Prediction[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231108

嵌入多阶泰勒微分知识的多尺度注意力循环网络深度时空序列预测方法

doi: 10.11999/JEIT231108
基金项目: 陕西省气象局秦岭和黄土高原生态环境气象重点实验室开放研究基金(2021G-28)
详细信息
    作者简介:

    孙强:男,副教授,研究方向为情感计算、智慧气象与人机交互

    赵珂:女,硕士生,研究方向为智慧气象

    通讯作者:

    孙强 qsun@xaut.edu.cn

  • 中图分类号: TN957.52;TP391

Multi-scale Attention Recurrent Network with Multi-order Taylor Differential Knowledge for Deep Spatiotemporal Sequence Prediction

Funds: Open Research Fund of Key Laboratory of Ecology and Environment in Qinling and Loess Plateau of Shaanxi Meteorological Bureau (2021G-28)
  • 摘要: 融合先验物理知识的深度时空序列预测方法通常使用偏微分方程(PDE)进行建模,这种做法通常存在两大问题:(1)偏微分方程的近似精度低;(2)无法在循环网络中有效捕捉多种空间尺度的时空特征和时空序列的边缘相关空间信息。为此,提出了融合泰勒微分的卷积循环神经网络(TDI-CRNN)。首先,为了提高高阶偏微分方程的近似精度并缓解偏微分方程应用的局限性,设计了一种多阶泰勒近似物理模块。该模块首先使用泰勒展开式对输入序列作微分逼近,再将不同阶数之间的微分卷积层使用微分系数耦合,最后动态调整泰勒展开结果的截断阶数与微分项数。其次,为了捕获循环网络隐藏状态的多种空间尺度特征并更好地捕捉时空序列的边缘相关空间信息,设计了一种多尺度注意力循环模块(MSARM),在该模块的多尺度卷积空间注意力UNet(即MCSA-UNet)的卷积层中使用了多尺度卷积和空间注意力机制,目的是关注时空序列的局部空间区域。在Moving MNIST、KTH以及CIKM数据集上开展了大量实验,Moving MNIST数据集的均方误差(MSE)指标下降到42.7, 结构相似性指数(SSIM)提高到0.912;KTH数据集的SSIM和峰值信噪比(PSNR)分别提高到0.882和29.03;CIKM数据集上的临界成功指数(CSI)提高到0.515。最终的可视化和定量预测结果均验证了TDI-CRNN模型的合理性和有效性。
  • 图  1  ST-LSTM单元结构示意图[9]

    图  2  TDI-CRNN模型架构

    图  3  MSARM模块结构示意图

    图  4  MCSA-UNet模块结构示意图

    图  5  多阶泰勒近似物理模块结构示意图

    图  6  多层卷积耦合近似PDE示意图

    图  7  Moving MNIST数据集上的预测可视化示例图

    图  8  Moving MNIST数据集上不同时间步的逐帧预测MSE结果

    图  9  KTH 数据集的预测可视化示例图

    图  10  KTH数据集不同时间步的逐帧预测结果

    图  11  CIKM数据集的预测可视化示例图

    图  12  CIKM数据集上不同时间步的逐帧预测结果性能对比

    表  1  数据驱动的预测模型

    类别模型内涵
    模型名称模型思想
    基于门控机制或堆叠方式的改进模型ConvLSTM[5]提出使用卷积运算代替LSTM中的普通乘法运算
    Conv-TT-LSTM[8]提出了高阶卷积LSTM
    PredRNN[8]使用共享输出门实现无缝的记忆融合
    MIM[12]提出的网络能够同时捕捉平稳信息和非平稳信息
    E3D-LSTM[25]将三维卷积集成到循环网络中
    ZNet[27]提出新的堆叠方式,隐藏状态沿Z曲线更新
    IM-LSTM[28]设计了SIM模块,用于更新隐藏状态
    DFN[29]生成动态卷积学习,以实现自适应特征提取
    使用编码器-解码器架构的模型MCNet[30]在编码器-解码器以及ConvLSTM上进行预测
    STMFANet[31]提出空间小波分析模块,统一处理时空信息
    FRNN[32]堆叠多个循环单元层,得到自动编码器
    引入注意力机制的模型SA-ConvLSTM[7]加入自注意机制,捕获长程空间依赖关系
    CSAConvLSTM[33]加入自注意力机制,捕获全局时空特征
    下载: 导出CSV

    表  2  知识引导与数据驱动的预测模型

    类别模型内涵
    模型名称模型思想
    嵌入偏微分方程的深度预测网络PDE-Net[16]使用卷积核近似偏微分方程
    Advection-diffusion[21]通过卷积和常微分之间的关系进行预测
    CDNA[19]通过卷积运算预测多个离散分布
    概率预测模型VPN[22]引入独立假设估计视频的离散联合分布
    DDPAE[18]结构化概率模型自动分解出高维信息
    下载: 导出CSV

    表  3  Moving MNIST数据集上的实验结果(10个时间步上的平均预测结果)

    模型 SSIM $ \Delta $ MSE↓ $ \Delta $
    ConvLSTM[5]* 0.707 103.3
    IM-LSTM[28]* 0.876 +0.169 67.4 –35.9
    PredRNN[8]* 0.867 +0.160 56.8 –46.5
    Conv-TT-LSTM[8]* 0.905 +0.198 53.0 –50.3
    MIM[12]* 0.901 +0.194 44.2 –59.1
    SA-ConvLSTM[7]* 0.903 +0.196 43.9 –59.4
    LMC-memory[34]* 0.904 +0.197 42.9 –60.4
    PDE-Net[16]· 0.621 –0.086 160.2 +56.9
    CDNA[19]· 0.721 +0.014 97.4 –5.9
    VPN[22]· 0.870 +0.163 70.2 –33.1
    DDPAE[18]· 0.905 +0.198 43.5 –59.8
    MSARM(4层)× 0.873 +0.166 43.9 –59.4
    Taylor+ST-LSTM(4层)× 0.893 +0.186 44.2 –59.1
    TDI-CRNN 0.912 +0.205 42.7 –60.6
    下载: 导出CSV

    表  4  不同泰勒截断阶数和微分项数对模型性能的影响

    截断阶数 微分项数
    2 3 4
    3阶 MSE=45.80
    SSIM=0.886
    MSE=44.31
    SSIM=0.892
    MSE=45.26
    SSIM=0.884
    4阶 MSE=43.77
    SSIM=0.902
    MSE=42.71
    SSIM=0.910
    MSE=43.54
    SSIM=0.897
    5阶 MSE=46.27
    SSIM=0.886
    MSE=43.54
    SSIM=0.898
    MSE=46.514
    SSIM=0.880
    下载: 导出CSV

    表  5  KTH数据集的实验结果(20个时间步上的平均预测结果)

    模型SSIM$ \Delta $PSNR$ \Delta $
    ConvLSTM[5]*0.71223.58
    FRNN[32]*0.771+0.05926.12+2.54
    DFN[29]*0.794+0.08227.26+3.68
    ZNet[27]*0.817+0.10527.58+4.00
    MCNet[30]*0.804+0.09225.95+2.37
    PredRNN[8]*0.839+0.12727.55+3.97
    CSAConvLSTM[33]*0.840+0.12827.91+4.33
    STMFANet[31]*0.851+0.13927.24+3.66
    E3D-LSTM[25]*0.879+0.16729.31+5.73
    PDE-Net[16]·0.662-0.0522.45-1.13
    DDPAE[18]·0.845+0.13328.42+4.84
    TDI-CRNN0.882+0.17029.03+5.45
    下载: 导出CSV

    表  6  CIKM雷达回波数据集在十个时间步上的平均预测结果

    模型HSSCSIMSE↓
    $\tau \ge 5$$\tau \ge 15$$\tau \ge 30$平均值$ \Delta $$\tau \ge 5$$\tau \ge 15$$\tau \ge 30$平均值$ \Delta $$ \Delta $
    ConvLSTM[5]*0.6620.5690.2720.5010.7430.5570.1850.49594.7
    PredRNN[8]*0.6780.5710.2810.508+0.0070.7550.5690.1990.508+0.013104.1+9.3
    RC-LSTM[34]*0.6820.5800.2880.513+0.0120.7610.5710.2250.509+0.01488.2-9.2
    PDE-Net[16]·0.6640.5740.2750.503+0.0020.7490.5580.1920.501+0.00678.6-16.1
    Advection-diffusion[21]·0.6790.5790.2880.509+0.0080.7590.5710.2090.510+0.01555.4-39.3
    DDPAE[18]·0.6820.5810.2910.514+0.0130.7640.5720.2270.512+0.01743.2-51.5
    TDI-CRNN0.6850.5820.2930.516+0.0150.7670.5780.2600.515+0.0236.8-57.9
    下载: 导出CSV
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  • 收稿日期:  2023-10-11
  • 修回日期:  2024-04-29
  • 网络出版日期:  2024-05-15

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