ISSN 1000-1239 CN 11-1777/TP

• 网络技术 •

遗传算法优化回声状态网络的网络流量预测

1. 1(沈阳工业大学信息科学与工程学院 沈阳 110870); 2(东北大学信息科学与工程学院 沈阳 110819) (tianzhongda@126.com)
• 出版日期: 2015-05-01
• 基金资助:
基金项目：国家自然科学基金重点项目(61034005)；辽宁省博士科研启动基金项目（20141070）

Prediction Method for Network Traffic Based on Genetic Algorithm Optimized Echo State Network

Tian Zhongda1, Gao Xianwen2, Li Shujiang1, Wang Yanhong1

1. 1(College of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870); 2(College of Information Science and Engineering, Northeastern University, Shenyang 110819)
• Online: 2015-05-01

Abstract: Network traffic prediction is an important problem of network congestion control and network management. Network traffic time series have time-varying and nonlinear characteristics, So the prediction accuracy of traditional time series prediction method is relatively low and it is unable to establish accurate prediction model. Echo state network (ESN) has good performance in prediction and modeling of nonlinear chaotic system and is very suitable for network traffic time series prediction problem. In order to improve the prediction accuracy of network traffic, the network traffic nonlinear prediction method based on genetic algorithm optimized echo state network is proposed. Firstly, echo state network is used for network traffic predicton, then the genetic algorithm is used to optimize the parameters of the reservoir of the echo state network prediction model. Finally, the prediction accuracy of the prediction model is improved. The actual network traffic data from China Unicom in Liaoning Province is used for simulation verification. Three common prediction models including auto regressive integrated moving average (ARIMA) prediction model, Elman neural network prediction model and least square support vector machine (LSSVM) prediction model are compared, and the simulation results show that the proposed method has higher prediction accuracy with smaller prediction error and it can describe the network traffic characteristics of complex changes.