Research on Resource Prediction of Space-based Information Network Based on Improved GRU Algorithm

被引：0
|
作者：
Geng R. [1 ]
Wu Y.-Q. [1 ]
Xiao Q.-Q. [1 ]
Xu S. [1 ]
机构：
[1] School of Computer Science & Engineering, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2023年 / 44卷 / 03期
关键词：
Adam optimizer; Dropout technology; GRU (gated recurrent unit) network; resource prediction; space-based information network;
D O I：
10.12068/j.issn.1005-3026.2023.03.001
中图分类号：
G2 [信息与知识传播];
学科分类号：
05 ; 0503 ;
摘要：
In orde to improve the resource utilization of space-based information metwork efficiency, a resource prediction model of space-based information network was presented based on the improved GRU (gated recurrent unit) algorithm. Firstly, a hierarchical three-level resource prediction framework was proposed to solve the problem of long delay in space-based environment. Then, Adam optimizer was used to optimize the learning rate of GRU network. Finally, Dropout technology was introduced to solve the over-fitting problem in the network. The experiments simulated the prediction of various space-based resources under different prediction models, and compared the prediction accuracy of GRU model under different optimizers. The results show that the resource prediction model based on improved GRU network has better performance. © 2023 Northeastern University. All rights reserved.
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收藏
页码：305 / 314
页数：9
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