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

作     者：Tian Zhongda Li Shujiang Wang Yanhong Wang Xiangdong 

作者机构：School of Information Science and EngineeringShenyang University of Technology 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2018年第25卷第3期

页      面：33-44页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081201[工学-计算机系统结构] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Science Research Project of Liaoning Education Department (LGD2016009) the Natural Science Foundation of Liaoning Province of China (20170540686) the State Key Program of Basic Research of China (2016YFD0700104-02) 

主　　题：error minimized extreme learning machine improved artificial bee colony algorithm network traffic prediction 

摘      要：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.