An adaptive neural network model for VBR video traffic prediction is proposed in this paper.Firstly

adaptive training and pruning algorithm based on Extended Kalman Filtering(EKF) approach is used to train the Time Delay Neural Network(TDNN).By pruning the unimportant hidden weights

the corresponding redundant hidden neurons can be deleted

as a result a compact TDNN architecture can be obtained.The pruning process results in better generalization ability and lower computational complexity for the online stage.During on-line training stage

the TDNN's weights will be updated using Recursive Least Square(RLS) algorithm according to current prediction error.Since EKF and RLS are second order algorithms

they can estimate the learning step automatically

faster convergence speed and more precise prediction can be obtained.By simulation and comparison

the adaptive neural network model proposed in this paper is shown to be promising and practically feasible in obtaining the best adaptive prediction of real-time VBR video traffic.