基于PSR-LSSVM的网络流量预测

为了提高网络流量预测精度,利用相空间重构和预测模型参数间的相互联系,提出一种遗传优化最小二乘支持向量机的网络流量预测方法。首先将相空间重构和最小二乘支持向量机参数作为遗传算法的个体,将模型预测精度作为个体适应度函数,然后通过遗传操作获得模型全局最优参数,最后通过网络流量仿真实验进行性能测试。结果表明,相对于传统预测方法,遗传优化最小二乘支持向量机提高了网络流量的预测精度,为网络流量预测提供了一种新的研究思路。

Network Traffic Prediction Based on Phase Space Reconstruction and Least Square Support Vector Machine

In order to improve the network traffic prediction accuracy, this paper proposes a network traffic prediction method based on least sctuare support vector machine(LSSVM) optimized by genetic algorithm which uses the relation between phase space reconstruction and parameters of prediction model. Firstly, phase space reconstruction and the parameters of LSSVM were used as an individual of genetic algorithm while the model prediction accuracy was used as the fitness function, and then global optimal parameters of the model were obtained by genetic algorithm, lastly, the simulation tests were carried out based on network traffic data. The results show that, compared with the traditional forecasting methods, the proposed model improves the prediction accuracy of network traffic and provide a new research thought for network traffic prediction.