A WAN Network Traffic Prediction Model Based on Wavelet Transform and FIR Neural Networks
In this paper, a WAN network traffic prediction model based on wavelet transform and FIR neural networks is proposed. The model employs wavelet transform which decomposes the traffic into high frequency coefficients and low frequency coefficients , then these different frequency coefficients are reconstructed by single branch to the high frequency traffic parts and the low frequency traffic parts which are sent individually into different FIR neural networks for prediction. The synthesized outputs are the predicted results of the original network traffic. The experimental results with the real WAN network traffic show that the proposed model has much better prediction performance compared to the wavelet neural networks and the FIR neural networks.
NETL
NSTL
万方数据
维普
