Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

doi:10.19682/j.cnki.1005-8885.2018.0014

JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2018, Vol. 25 ›› Issue (3): 33-44.doi: 10.19682/j.cnki.1005-8885.2018.0014

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Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

Received:2017-08-29 Revised:2018-01-14 Online:2018-06-29 Published:2018-06-30

Abstract

Abstract: In order to overcome the poor generalization ability and low accuracy of traditional network traffic prediction methods, a prediction method based on improved artificial bee colony (ABC) algorithm optimized error minimized extreme learning machine (EM-ELM) is proposed. EM-ELM has good generalization ability. But many useless neurons in EM-ELM have little influences on the final network output, and reduce the efficiency of the algorithm. Based on the EM-ELM, an improved ABC algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons. Network complexity is reduced. The efficiency of the algorithm is improved. The stability and convergence property of the proposed prediction method are proved. The proposed prediction method is used in the prediction of network traffic. In the simulation, the actual collected network traffic is used as the research object. Compared with other prediction methods, the simulation results show that the proposed prediction method reduces the training time of the prediction model, decreases the number of hidden layer nodes. The proposed prediction method has higher prediction accuracy and reliable performance. At the same time, the performance indicators are improved.

Key words: error minimized extreme learning machine, improved artificial bee colony algorithm, network traffic, prediction

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Network traffic prediction method based on improved ABC algorithm optimized EM-ELM

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