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基于相空间重构的网络流量RBF神经网络预测
引用本文: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.
作者姓名:陆锦军  王执铨
作者单位:1. 南京理工大学自动化学院,南京,210094,中国;南通职业大学现代教育技术中心,南通,226007,中国
2. 南京理工大学自动化学院,南京,210094,中国
基金项目:国家自然科学基金;江苏省自然科学基金;高等学校博士学科点专项科研项目
摘    要:应用混沌理论,分析了网络流量,用单变量的网络流量时闯序列重构与网络动力系统等距同构的相空间,进而计算了实际网络的关维数和Lyapunov指数,并证实了网络流量存在混沌特性;据此建立了基于径向基函数(RBF)神经网络的模型,并对实际网络数据流进行了预测。仿真结果表明,相对于其他前馈神经网络预测,基于混沌理论的RBF神经网络预测方法学习速度快,预测精度高。

关 键 词:混沌理论  重构相空间  Lyapunov指数  网络流量  RBF神经网络
收稿时间:04 26 2006 12:00AM
修稿时间:10 1 2006 12:00AM

INTERNET TRAFFIC DATA FLOW FORECAST BY RBF NEURAL NETWORK BASED ON PHASE SPACE RECONSTRUCTION
Lu Jinjun,Wang Zhiquan.INTERNET TRAFFIC DATA FLOW FORECAST BY RBF NEURAL NETWORK BASED ON PHASE SPACE RECONSTRUCTION[J].Transactions of Nanjing University of Aeronautics & Astronautics,2006,23(4):316-322.
Authors:Lu Jinjun  Wang Zhiquan
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.
Keywords:chaos theory  phase space reconstruction  Lyapunov exponent  Internet data flow  radial basis function neural network
本文献已被 CNKI 维普 万方数据 等数据库收录!
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