计算机工程与应用Issue(15)：101-104,156,5.DOI:10.3778/j.issn.1002-8331.1201-0014
混沌理论和LSSVM相结合的网络流量预测
Network traffic forecasting based on chaotic theory and Least Squares Support Vector Machine
张文金 1许爱军1
作者信息
- 1. 广州铁路职业技术学院 教育技术中心，广州 510430
- 折叠
摘要
Abstract
In order to improve the prediction accuracy of network traffic, this paper proposes a network traffic forecasting method based on chaotic theory and Least Squares Support Vector Machine. Phase space reconstruction is used to reconstruct the network traffic time series and restore the network flow evolution path, and then the network traffic time series are modeled and trained by Least Squares Support Vector Machines which has good nonlinear forecasting ability, and the parameters of Least Squares Support Vector Machine are optimized by chaotic particle swarm algorithm to obtain the optimal network traffic forecasting model. The forecasting method is tested by the network traffic time series data. The results show that the method can well depict the network flow change trend and improves the forecasting accuracy of network traffic whose forecasting performance is superior to the traditional forecasting method.关键词
混沌理论/最小二乘支持向量机/网络流量/预测模型Key words
chaotic theory/Least Squares Support Vector Machine(LSSVM)/network traffic/forecasting model分类
信息技术与安全科学引用本文复制引用
张文金,许爱军..混沌理论和LSSVM相结合的网络流量预测[J].计算机工程与应用,2013,(15):101-104,156,5.
