基于改进LSTM算法的天地一体化信息网络流量预测

doi:10.11959/j.issn.2096-8930.20200208

Abstract

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.

Key words: space-integrated-ground information network, traffi c prediction of network, deep learning, traffi c autocorrelation

CLC Number:

TN929

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.

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References 15

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算法类型	RMSE	MAPE	训练时间/s
本文改进的LSTM算法	15.38	5.35%	64
RNN算法	374.53	60.77%	91
ARIMA算法	37.71	11.93%	-
LSTM算法	62.14	31.52%	72

Traffic Prediction of Space-Integrated-Ground Information Network Based on Improved LSTM Algorithm

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