Research On Network Traffic Prediction Algorithm Based On Improved Neural Network

Realizing the network Qos control for network management is of the same importantance as the network maintence,and network traffic prediction is very important to realization of the network Qos control;Another, the application of network traffic prediction in intrstion detection system Caused concern more and more. network traffic prediction is quite a useful research which have essential reality meanings.The request for the quality of real-time is more than other aspects for the estimation of the network discharge .Except for that,accutacy is also an important sign .This text is based on the model of the neural network prediction .To improve the basic function ,we import the wavelet function to create a new network traffic prediction model .Also we establish new valid integration prediction model based on the different characters of several neural-networks.I give the correspond algorithm of the above two models , Simulation experiment certificate that the model has lower error,and the model can be used generally.The content of this text is aimed at the improvement of the neural network traffic prediction model, and the design of the correspond estimating step, main research results are:1.I import the wavelet function as the passing function of the hidden layer in the BP neural network traffic prediction model,create a new wavelet neural network traffic prediction model,design the related prediction algorithm, and withdraw the true network traffic to verificate the model.In this way ,we improve the training speed of the neural network and the target of improvement comes true.2.We made valid integration based on the advantages of the model: the line neural network , Elman neural network, RBF neural network ,and BP neural network .We make use of the advantages of the above several of neraul network models and get satisfacting result by building a model based on several of neraul networks and doing the imitating verification for this new network traffic prediction model using the true network traffic.