关键词: 网络流量预测, 相空间重构, 神经网络, 粒子群优化算法

Abstract: In order to improve the prediction precision of network traffic, this paper proposes a nonlinear network traffic prediction model based on parameters joint optimization algorithm, which uses the relationship between the phase space reconstruction and prediction model parameters. The phase space reconstruction and prediction model parameters are taken as particle of Improved Particle Swarm Optimization algorithm（IPSO） while the prediction accuracy of network traffic as the evaluation function of CPSO, and then, the optimization parameters are obtained by collaboration among particles. The optimal nonlinear network traffic prediction model is built according to the parameters. The performance of the proposed model is tested by network traffic data. The results show that the proposed method has improved the prediction precision of network traffic compared with the traditional parameters optimization algorithm; it has provided a new way for the nonlinear prediction problem.

Key words: network traffic prediction, phase space reconstruction, neural network, Particle Swarm Optimization（PSO）