基于小波分析和Hopfield的网络流量预测

NETWORK TRAFFIC PREDICTION BASED ON WAVELET ANALYSIS AND HOPFIELD NEURAL NETWORK

针对传统单一的网络流量模型不能对网络流量的复杂特性进行精确模拟的问题,提出一种基于αTrous小波分析和Hopfield神经网络的组合模型对网络流量进行预测。首先对网络流量进行归一化处理并采用αTrous小波变换;然后对小波单支进行重构,并将低频成分送入AR模型高频成分送入Hopfield神经网络进行建模预测;最后对各分量进行合成得到预测值。仿真实验结果表明,该模型提高了预测精度,并且具有很好的网络适应性。

Aiming at the problem of traditional single network traffic model that it cannot accurately simulate the complex characteristics of network traffic, in this paper we propose a composite model to predict the network traffic which is based on αTrous wavelet analysis and Hopfield neural network, First, the network traffic is normalised and applied the αTrous wavelet transform; then the wavelet single is reconstructed, and for modelling and prediction, its low frequency component is sent into AR model, its high frequency component is sent into Hopfield neural network; Finally, the components are composited to obtain the predictive value. Simulation experimental results show that the model improves the prediction accuracy and has good adaptability to the network.