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

Qi Feng, Jinhui Zhang, Xiaoguang Gao, Maoqing Li, Chenxi Ning

科研成果: 书/报告/会议事项章节会议稿件同行评审

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.

源语言英语
主期刊名2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023
出版商Institute of Electrical and Electronics Engineers Inc.
21-27
页数7
ISBN(电子版)9798350345650
DOI
出版状态已出版 - 2023
活动8th International Conference on Control and Robotics Engineering, ICCRE 2023 - Niigata, 日本
期限: 21 4月 202323 4月 2023

出版系列

姓名2023 8th International Conference on Control and Robotics Engineering, ICCRE 2023

会议

会议8th International Conference on Control and Robotics Engineering, ICCRE 2023
国家/地区日本
Niigata
时期21/04/2323/04/23

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