用神经网络实现VBR视频通信量的在线预测

VBR(Varible Bit Rate)视频信号具有时变性、非线性和突发性等特点,实现该信号通信量的高精度预测难度较大.针对以上问题,本文提出了一种用于VBR视频通信量预测的自适应神经网络模型,网络训练采用离线与在线相结合的方式,同时通过删除不重要的权重,以优化网络的拓扑结构,提高网络的推广能力,降低网络在线学习的计算复杂度;对VBR视频通信量预测的模拟结果表明该模型具有高的预测精度,并能满足通信系统对预测实时性的要求.

On-Line VBR Video Traffic Prediction Using Neural Network

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.