南京航空航天大学学报（英文版）2006，Vol.23Issue(4)：316-322,7.
基于相空间重构的网络流量RBF神经网络预测
INTERNET TRAFFIC DATA FLOW FORECAST BY RBF NEURAL NETWORK BASED ON PHASE SPACE RECONSTRUCTION
摘要
Abstract
Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated,and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is constructed based on radial basis function (RBF) to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.关键词
混沌理论/重构相空间/Lyapunov指数/网络流量/RBF神经网络Key words
chaos theory/phase space reconstruction/Lyapunov exponent/Internet data flow/radial basis function neural network分类
信息技术与安全科学引用本文复制引用
陆锦军,王执铨..基于相空间重构的网络流量RBF神经网络预测[J].南京航空航天大学学报（英文版）,2006,23(4):316-322,7.基金项目
国家自然科学基金(6037406)资助项目 （6037406）
江苏省自然科学基金(BK2004132)资助项目 （BK2004132）
高等学校博士学科点专项科研基金(202088025)资助项目. （202088025）
