文献类型：期刊文献

中文题名：基于PSR-SVM的网络流量预测

英文题名：Network traffic prediction based on phase space reconstruction and support vector machine

作者：李玉霞[1];沈桂兰[1]

第一作者：李玉霞

机构：[1]北京联合大学商务学院

第一机构：北京联合大学商务学院

年份：2013

卷号：34

期号：11

起止页码：3796-3800

中文期刊名：计算机工程与设计

外文期刊名：Computer Engineering and Design

收录：CSTPCD;;北大核心:【北大核心2011】；CSCD:【CSCD_E2013_2014】；

基金：北京自然科学基金项目(9251009001000021)

语种：中文

中文关键词：支持向量机;相空间重构;网络流量预测;混沌理论;灰色模型

外文关键词：support vector machine; phase space reconstruction; network traffic prediction; chaotic theory; grey model

摘要：为了提高网络流量的预测精度,提出一种相空间重构和支持向量机相结合的网络流量预测模型(PSR-SVM)。通过相空间重构对网络流量序列进行重构,重构网络流量序列输入到支持向量机进行建模和预测,利用具体网络流量数据进行仿真实验,与BP神经网络、灰色模型预测结果进行对比。实验结果表明,相对于对比模型,PSR-SVM提高了网络流量的预测精度和稳定性,能够很好满足网络流量预测实时性和高精度要求。
To improve the prediction accuracy of network flow, a network traffic prediction model is proposed by integrating support vector machine with phase space reconstruction. Firstly, the network flow sequence is reconstructed by phase space reconstruction, and then reconstructed network traffic sequence is inputted to the support vector regression machine to model and predict, finally, the simulation experiment is carried out on network traffic data. The experimental results show that the proposed model improves the prediction accuracy and stability of network traffic compared with other models, it can fulfill the prediction time and high precision of network traffic requirements.