基于灰色径向基神经网络模型的流量预测与分析

根据神经网络能有效修正灰色预测模型的思路，本文提出了基于灰色系统及径向基神经网络的组合预测模型。通过采集园区节点交换机的流量数据，在分析网络流量时间序列特性的基础上建立灰色GM（1，1）模型，并采用径向基神经网络对预测模型残差进行修正。实验结果和仿真实验表明，组合模型效果及预测精度远优于单一灰色预测模型。

Traffic Prediction and Analysis Based on the Grey Radial Basis Function Neural Network Model

According to the idea that neural networks can effectively modify grey prediction models,the paper proposes a combination prediction model based on radial basis function neural networks.By collecting the network switching data of the campus,the paper builds a grey GM(1,1)model based on analysing the netwok traffic time sequence characteristics,and corrects the remain differences of the model based on adopting the RBF neural network.The experimental results show that the novel model is more effective than the single grey model.