基于小波神经网络的流量自回归预测研究

ON FORECASTING TRAFFIC AUTOREGRESSION BASED ON WAVELET NEURAL NETWORK

通过应用系统的历史网络流量训练小波神经网络的各个参数,最终确定收敛的小波神经网络的结构。证明流量的自回归短期预测是个NP问题,分析小波神经网络预测算法的时间复杂度。通过实验结果表明,采用小波神经网络相对于BP、GRNN、解决此类NP问题具有良好预测效果和收敛速度。

By employing historic network traffic of the application system to train various parameters of wavelet neural network （ WNN）, we finally determine the structure of the convergent WNN. In this paper we prove that the short-term forecast of autoregression of traffic is an NP problem, we analyse the time complexity of WNN forecast algorithm, and demonstrate through experimental results that using WNN to solve such NP problem has better forecasting effect and faster convergence speed than BP and GRNN.