Research on Deep Spatio-Temporal Model and Its Application in Situation Prediction

被引：0
作者：
Feng, Qi [1 ]
Zhang, Jinhui [1 ]
Gao, Xiaoguang [1 ]
Li, Maoqing [2 ]
Ning, Chenxi [3 ]
机构：
[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Peoples R China
[2] Shaanxi Coal Chem Ind Technol Res Inst, Xian, Peoples R China
[3] Xian Yunlei Technol Intelligence Co Ltd, Xian, Peoples R China
来源：
2023 8TH INTERNATIONAL CONFERENCE ON CONTROL AND ROBOTICS ENGINEERING, ICCRE | 2023年
关键词：
situation prediction; deep learning; spatio-temporal model; attention mechanism;
D O I：
10.1109/ICCRE57112.2023.10155597
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Situation prediction refers to predicting the state information of things in the future on the basis of existing information, and situational information contains complex laws of time and space. Traditional methods only consider a single factor or separate time and space. At the same time, due to the limitations of traditional algorithms, it is not possible to accurately predict air combat events with long interval dependencies. In order to solve these problems, we propose a deep spatio-temporal model based on the dynamic graph convolution and attention mechanisms. The model extracts and analyzes the features of space and time respectively. Experimental results show that the model proposed in this paper has more stable training process and higher prediction accuracy.
引用
收藏
页码：21 / 27
页数：7
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