VBR MPEG视频源流量的智能集成预测模型

由于VBR视频流量的预测能力是直接关系缓冲区设计、动态带宽分配及拥塞控制等提高网络服务质量的关键因素,因此,以多媒体主要应用形式VBRMPEG视频源为研究对象,提出了一种基于模糊神经网络的视频流量智能预测模型。通过设计模糊预测器减少输出比特流的预测误差,采用神经网络减少多步预测的计算量。仿真试验表明,与标准AR模型预测结果相比,该模型显著提高了预测的准确度和可靠性,且易于推广使用。

An Intelligent Integrated Traffic Prediction Model for VBR MPEG Video Sources

Owing to the capability to predict VBR video traffic can significantly improve the effectiveness of numerous management tasks,including dynamic bandwidth allocation and congestion control,an intelligent prediction model is proposed for VBR MPEG videos,a major multimedia application,based on fuzzy and neural network.The prediction error is reduced by the fuzzy predictor,and the implementation of neural network is used for lower computation of multistep prediction.Simultion results show that the proposed method is able to predict the original traffic more accurately than the conventional AR method and can be easily applied.