Traffic Prediction of Space-Integrated-Ground Information Network Based on Improved LSTM Algorithm
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 impr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Post&Telecom Press Co.,LTD
2020-12-01
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| Series: | 天地一体化信息网络 |
| Subjects: | |
| Online Access: | http://www.j-sigin.com.cn/thesisDetails#10.11959/j.issn.2096-8930.20200208 |
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| Summary: | 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. |
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| ISSN: | 2096-8930 |