基于改进LSTM算法的天地一体化信息网络流量预测

潘成胜; 王羽夫; 杨力

doi:10.11959/j.issn.2096-8930.20200208

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基于改进LSTM算法的天地一体化信息网络流量预测

专题：天地一体化信息网络体系架构 | 更新时间：2024-06-03

- 基于改进LSTM算法的天地一体化信息网络流量预测
- Traffic Prediction of Space-Integrated-Ground Information Network Based on Improved LSTM Algorithm
- 天地一体化信息网络 2020年1卷第2期页码：57-65
- 作者机构：
  
  1. 大连大学通信与网络重点实验室，辽宁大连 116622
  2. 南京信息工程大学，江苏南京 210044
- 作者简介：
  
  [ "潘成胜（1962-），男，大连大学通信与网络重点实验室教授，南京信息工程大学电子信息工程学院教授，主要研究方向为一体化网络系统与网络协议、一体化指挥系统的网络理论与技术。" ]
  [ "王羽夫（1996-），男，大连大学通信与网络重点实验室研究生，主要研究方向为一体化智能网络流量预测技术。" ]
  [ "杨力（1982-），女，大连大学通信与网络重点实验室负责人，主要研究方向为空间信息网络传输技术、无线通信网络协议理论与方法。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-8930.20200208
  中图分类号： TN929
- 网络出版日期：2020-12，
  
  纸质出版日期：2020-12-20
- 稿件说明：
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潘成胜, 王羽夫, 杨力. 基于改进LSTM算法的天地一体化信息网络流量预测[J]. 天地一体化信息网络, 2020,1(2):57-65.

Chengsheng PAN, Yufu WANG, Li YANG. Traffic Prediction of Space-Integrated-Ground Information Network Based on Improved LSTM Algorithm[J]. Space-integrated-ground information networks, 2020, 1(2): 57-65.
潘成胜, 王羽夫, 杨力. 基于改进LSTM算法的天地一体化信息网络流量预测[J]. 天地一体化信息网络, 2020,1(2):57-65. DOI： 10.11959/j.issn.2096-8930.20200208.

Chengsheng PAN, Yufu WANG, Li YANG. Traffic Prediction of Space-Integrated-Ground Information Network Based on Improved LSTM Algorithm[J]. Space-integrated-ground information networks, 2020, 1(2): 57-65. DOI： 10.11959/j.issn.2096-8930.20200208.

摘要

天地一体化信息网络由于存在流量突发性强、拓扑时变等问题使得通信易产生中断，流量波动不平稳导致其流量预测难度远高于地面网络。针对该问题，提出一种改进的LSTM算法，首先分析流量序列滞后变量对预测值的影响，判断流量自相关度；其次，采用以预测值代替中断的方式，消除训练集的噪声和断点；最后，使用Dropout算法减少了噪声和神经网络过拟合带来的影响，准确预测天地一体化信息网络流量数据。仿真实验表明，在OPNET仿真环境中，该算法相较于其他算法准确性提升了59.21%，算法训练速度提升了11.11%，能够为天地一体化信息网络统筹调度提供有效的数据支撑。

Abstract

The space-integrated-ground information network is easy to interrupt and the traffi c fl uctuation is not stable due to the problems of high traffi c burst and topological time-varying

which makes the traffi c prediction diffi cult much higher than the ground.In order to solve this problem

an improved LSTM algorithm was put forward.Firstly

the traffi c autocorrelation was judged by analyzd the infl uence of the lag variable of traffi c sequence on the predicted value; Secondly

the noise and breakpoint of the training set were eliminated by replacing the interruption with the predicted value; Finally

Dropout algorithm was used to reduce the impact of noise and neural network over fi tting

and accurately predict the traffi c data of the integrated intelligent network.The simulation results showed that in OPNET simulation environment

compared with other algorithms

the accuracy of this algorithm was improved by 59.21%

and the training speed of the algorithm was improved by 11.11%

which could provide eff ective data support for the overall scheduling of the integrated intelligent network.

关键词

Keywords

references

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