关键词: 飞蛾捕焰算法, 网络流量预测, 小波包分解, 神经网络, 预测计算

Abstract: Traditional BP neural network has low prediction accuracy and weak generalization ability for network traffic time series.Therefor,a new method for optimizing BP neural network is proposed.The network traffic is decomposed into multi-channel series through wavelet packet decomposition,and the neural network optimized by moth crisscross chaos flame capturing algorithm is employed to forecast the decomposed sub-series.The predicted values of each sub-series are superimposed and the actual prediction results are obtained by reconstruction.Simulation results show that compared with the traditional BP neural network prediction method,this method can capture the variation law of network traffic,and it has good prediction accuracy,stability and generalization ability.

Key words: moth-flame algorithm, network traffic prediction, wavelet packet decomposition, neural network, prediction calculation