摘要
作为数字媒体网络视频通信的主要方式,VBR MPEG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.因此针对MPEG视频流的复杂特性,充分利用人工智能方法的优势,提出并建立了基于模糊神经网络的智能集成VBR MPEG视频流量预测模型.采用模糊预测模型提高预测精度,利用神经网络解决预测的实时性问题.实验结果表明,与标准AR预测模型相比,该模型预测的准确度和可靠性显著提高,且算法简单易于推广到其他方法中使用.
As a main video transmission mode for digital media networks, the capability to predict VBR video traffic can significantly improve the effectiveness of quality of services. Therefore, aiming at the complex characteristics of MPEG videos, a novel intelligent integrated traffic prediction model is proposed based on fuzzy and neural network. The prediction error is reduced by the fuzzy predictor, and the implementation of neural network is used to lower prediction computation for real time. Simulation results show that the proposed method is able to predict the original traffic more accurately than the normal AR method and can be easily applied into other methods.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2006年第5期833-836,共4页
Acta Electronica Sinica
基金
国家重点自然科学基金(No.60432030)
国家杰出青年科学基金(No.60525111)
关键词
可变比特视频流
视频流量
模糊神经网络
智能集成
预测模型
variable bit rate of moving picture experts group （ VBR MPEG）
video traffic
fuzzy neural network（FNN）
intelligent integrated
prediction model
