Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine
Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (8): 319-322.doi: 10.11896/jsjkx.190800075

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Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine

CAO Su-e, YANG Ze-min   

  1. School of Computer and Network Engineering, Shanxi Datong University, Datong, Shanxi 037009, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:CAO Su-e, born in 1976, master, experimenter.Her main research interests include data processing and data mining.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (11871314) and National Natural Science Youth Foundation of China (11605107).

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Abstract: In order to solve the problems existing in the current wireless network traffic prediction process and improve the accuracy of wireless network traffic prediction, a wireless network traffic prediction model based on clustering analysis algorithm and optimized support vector machine is proposed.Firstly, data sets of wireless network traffic are collected and clustering analysis algorithm is used to construct the training sample set.And then, support vector machine is used to learn the training samples of wireless network traffic, and cuckoo search algorithms is introduced to optimize the parameters of support vector.Thus, the prediction model of wireless network traffic is established.Finally, the effectiveness of the model is analyzed through a specific example of wireless network traffic prediction.The results show that the proposed model has high prediction accuracy, improves the efficiency of wireless network traffic modeling, and the prediction effect of wireless network traffic is better than the current classical wireless network traffic prediction models, which has more significant advantages.

Key words: Clustering analysis algorithm, Cuckoo search algorithms, Support vector machine, Traffic prediction, Wireless network

CLC Number: 

  • TP391
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