摘要： 应用相空间重构理论，研究了网络数据流的混沌特性，计算了实际网络数据流的关维数、Lyapunov指数，证实网络数据流存在混沌现象；据此建立了基于径向基函数(RBF)预测模型，对实际网络数据流进行预测。仿真实验表明，相对于反向传播(BP)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,
网络数据流,
RBF神经网络
Abstract: Chaotic characteristics of the Internet traffic data flows is studied on the theory of the phase space reconstruction, and some parameters such as correlative dimension and Lyapunov exponent are computed, the Internet traffic chaos phenomena lying in Internet traffic data flows are demonstrated. A radial basic function(RBF) neutral network model is constructed to forecast the Internet traffic data flows. The simulation results show that the forecast method of the RBF neutral network compared with the forecast method of back propagation (BP) neutral network has faster learning capacity and higher accuracy of forecast.
Key words:
Chaos theory,
Phase space reconstruction,
Internet data flows,
RBF neutral network
陆锦军;王执铨. 基于混沌理论的网络数据流RBF神经网络预测[J]. 计算机工程, 2006, 32(23): 100-103.
LU Jinjun; WANG Zhiquan. Study of Internet Traffic Data Flow Forecast of RBF Neutral Network Based on Chaos Theory[J]. Computer Engineering, 2006, 32(23): 100-103.