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Received:February 19, 2014 Revised:April 01, 2014
Received:February 19, 2014 Revised:April 01, 2014
中文摘要: 为了提高网络流量的预测精度，考虑到网络流量的长相关、非线性等特性，提出一种粒子群算法优化最小二乘支持向量机参数的网络流量预测模型（PSO-LSSVM）. 首先将最小二乘支持向量机参数作为粒子的位置向量，然后利用粒子群算法找到模型的最优参数，最后采用最优参数最小二乘支持向量机建立网络流量预测模型. 仿真结果表明，相对于参比模型，PSO-LSSVM能够获得更高的网络流量预测精度，更能准确描述网络流量变化规律.
Abstract:Network traffic had long related and nonlinear characteristics, in order to improve the prediction accuracy of network traffic, this paper proposed a network traffic prediction method based on particle swarm algorithm optimizing the parameters of least square support vector machine. Parameters of least square support vector machine were taken as the position vector of particle, and then the particle swarm algorithm is used to find the optimal parameters of the model, finally, the prediction model of traffic model is established based on least square support vector machine with the optimal parameters. The simulation results showed that the proposed model had improved prediction accuracy ompared with other network traffic prediction models and could more accurately describe the change rule of network traffic.
keywords: prediction model network traffic particle swarm optimization least square support vector machine
文章编号： 中图分类号： 文献标志码：
基金项目:
| Author Name | Affiliation |
| LIU Chun | Network Management Center, Sichuan College of Architectural Technology, Deyang 618000, China |
| Author Name | Affiliation |
| LIU Chun | Network Management Center, Sichuan College of Architectural Technology, Deyang 618000, China |
引用文本：
刘春.基于PSO-LSSVM的网络流量预测模型.计算机系统应用,2014,23(10):147-151
LIU Chun.Network Traffic Prediction Method Based on Particle Swarm Algorithm Optimizing Least Square Support Vector Machine.COMPUTER SYSTEMS APPLICATIONS,2014,23(10):147-151
刘春.基于PSO-LSSVM的网络流量预测模型.计算机系统应用,2014,23(10):147-151
LIU Chun.Network Traffic Prediction Method Based on Particle Swarm Algorithm Optimizing Least Square Support Vector Machine.COMPUTER SYSTEMS APPLICATIONS,2014,23(10):147-151
