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一种融合多源动态时空特征的战术互联网流量预测模型

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战术互联网流量具有极强的动态时空特征,且与天气、高程等外部特征紧密相关,现有的网络流量预测模型不能很好地提取其复杂特征,提出了一种融合多源动态时空特征的战术互联网流量预测模型.首先,将外部特征与流量特征融合为多源特征;然后,提取当前时刻网络流量的空间特征,并对随时间变化的卷积权重迭代更新得到不同时间片下的空间特征信息;最后,通过时间卷积聚合当前和历史时刻的空间信息以预测下一时刻的多源动态时空流量.相比单一的基础模型,该方法在平均绝对误差(MAE)、均方根误差(RMSE)和决定系数(R2)三种评估指标中效果均更好.
A tactical Internet incorporating multi-source dynamic spatio-temporal characteristics traffic prediction model
Tactical Internet traffic has extremely dynamic spatio-temporal features and is closely related to external features such as weather and elevation,existing network traffic prediction models can not extract its complex features well,a tactical Internet traffic prediction model that fuses multi-source dynamic spatio-temporal features is proposed.Firstly,external features are fused with traffic features as multi-source features;then the spatial features of network traffic at the current mo-ment are extracted and the convolution weights over time are iteratively updated to obtain the spatial feature information under different time slices;finally,the spatial information of the current and historical moments are aggregated by the temporal convolution layer to predict the multi-source dynamic spatio-temporal traffic at the next moment.Compared with the single base model,the proposed method is better in all three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE)and coefficient of determination(R2).

network traffic predictiontactical Internetmulti-source dynamic spatial and temporal characteristicsneural network

刘思晓、周明、郑富中、田罗庚、石永琪

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国防科技大学信息通信学院, 湖北 武汉 430019

陆军步兵学院, 江西 南昌 330000

网络流量预测 战术互联网 多源动态时空特征 神经网络

2024

指挥控制与仿真
中国船舶重工集团公司 第七一六研究所

指挥控制与仿真

CSTPCD
影响因子:0.309
ISSN:1673-3819
年,卷(期):2024.46(1)
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