基于PSO-LSSVM的网络流量预测模型

摘要

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.%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。