基于kohonen神经网络的组合式流量预测模型.pdf

Hans Journal of Wireless Communications 无线通信, 2014, 4, 113-119 Published Online December 2014 in Hans. /journal/hjwc /10.12677/hjwc.2014.46018 Combined Prediction Model of Network Traffic Based on Kohonen Neural Network Xingle Tang, Wensheng Sun, Jinsong Yao School of Communication Engineering, Hangzhou Dianzi University, Hangzhou Email: nd th th Received: Oct. 22 , 2014; revised: Nov. 16 , 2014; accepted: Nov. 25 , 2014 Copyright © 2014 by authors and Hans Publishers Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). /licenses/by/4.0/ Abstract Considering that the original prediction model whose accuracy is low, and which highly depends on the training data and can’t well described the characteristics of network traffic, we proposed a mixed traffic prediction model. The model is based on the Kohonen neural network feartures, that is, quickly learning rate, highly classification accuracy and strongly anti-noise. By wavelet trans- forming, we decompose the network traffic into high frequency part and the low frequency part, and the high frequency part is dealt by using Kohonen neural network prediction model, the low frequency part by using autoregressive AR model to predict by using Matlab to simulat. Through the experiment we conclude this combination prediction model can improve the prediction accu- racy on multiple time scales and the nonlinear changing network traffic. Keywords Wavelet Transform, Neural Network, Traffic Prediction, Self-Organizing Mapping 基于kohonen神经网络的组合式 流量预测模型 唐幸乐，孙文胜，姚劲松 杭州电子科技大学通信工程学院，杭州 Email: 收稿日期：2014年10月22 日；修回日期：2014年11月16 日；录用日期：2014年11月25 日