基于灰色神经网络和自适应网络过滤的流量预测模型

网络流量是衡量网络运行负荷和状态的重要参数,也是网络规划、流量管理等方面起着重要作用的重要参数。在流量管理中,流量模型用于评价接入控制机制和预测网络性能。在灰色神经网络研究的基础上,提出一种新的网络预测方法,通过自适应过滤法对灰色神经组合模型时产生的残差进行修正,从而达到比较精确的效果。实验结果表明,该方法有效可行。

Network Traffic Prediction Based on Adaptive Filter and Grey Neural Network Integrated Model

The network traffic is the important parameter that measures the burden of network movement and network appearance.It also plays an important role in network layout,traffic anagement.In traffic management,traffic model is used to evaluate the mechanism of join control and predict network performance.The grey model and neural network have good effect in reflecting the variable trend of data.A new network traffic prediction method is put forward.This method uses grey neural network model to conduct prediction according to the historical data of network traffic,and also uses adaptive filtering method to modify the residual error producted by grey neural network Model,which achieves a higher accuracy.Moreover,this method integrates some characters of grey neural network model.It has higher value for application.Experimental results show this method is effective and feasible.