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用于VBR视频通信量预测的梯度径向基函数网络模型 被引量：2

A Neural Network Model for VBR Video Traffic Prediction
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摘要 提出采用梯度径向基函数(GRBF,gradientradialbasisfunction)神经网络实现VBR(variablebitrate)视频通信量的预测,由于GRBF神经网络采用差分输入,能够消除由于局部平均值随时间变化而造成的不稳定性,特别适合于非平稳时间序列预测。仿真结果显示,GRBF神经网络模型的预测误差(相对均方误差)为2.9×10-3,而其它几种常见预测模型的预测误差在(1.6～8.5)×10-2之间。 The gradient radial basis function(GRBF) neural networks was proposed for performing variable bit rete(VBR) traffic prediction.Since it takes differential signal as input,it can eliminate the adverse influence of variation of the local average of the signal.As a result,it gives significantly precise prediction compared with other prediction models.The simulation results show that the prediction error of GRBF neural networks is 2.9×10^(-3),while the prediction error of other models only are 10^(-2).
作者 李素梅 常胜江 苏晓星 熊涛 申金媛 张延炘
机构地区 南开大学现代光学研究所教育部光电信息技术重点实验室 中国科学院声学研究所声场声信息国家重点实验室
出处 《光电子．激光》 EI CAS CSCD 北大核心 2004年第7期814-817,共4页 Journal of Optoelectronics·Laser
基金 国家自然科学基金项目资助(60277022) 天津市自然科学基金重点资助项目(023800811)
关键词 梯度径向基函数 神经网络 VBR 视频通信量 正交最小平方 gradient radial basis function(GRBF) neural network variable bit rate(VBR) video traffic orthogonal least-mean square(OLS) algorithm
分类号 TN919.8 [电子电信—通信与信息系统]
  • 相关文献

参考文献11

  • 1A Adas.Using adaptive linear prediction to support real-time VBR video under RCBR network service model[J].IEEE/ACM Trans.Networking,1998,6(5):635-644.
  • 2S J Yoo.Efficient traffic prediction scheme for real-time VBR MPEG video transmission over high-speed networks[J].IEEE Trans.on Broadcasting.2002,48(1):10-18.
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  • 10苏晓星,常胜江,马天兵,申金媛,张延.一种用于ATM业务量预测的神经网络模型[J].光电子．激光,2003,14(8):842-845. 被引量：9

二级参考文献1

共引文献10

同被引文献15

  • 1李素梅,张延,常胜江,申金媛,李宜宾,王立.基于SVM实现人眼注视与否的探知[J].光电子．激光,2004,15(10):1229-1233. 被引量：10
  • 2熊涛,张便利,常胜江,申金媛,张延忻.基于Hopfield神经网络的信元调度多重输入队列ATM交换结构及算法[J].物理学报,2005,54(5):2435-2440. 被引量：9
  • 3A Adas.Using adaptive linear prediction to support real-time VBR video under RCBR network service model[J].IEEE/ACM Trans Networking,1998,6(5):635-644.
  • 4S J Yoo.Efficient traffic prediction scheme for real-time VBR MPEG video transmission over high-speed networks[J].IEEE Trans on Broadcasting,2002,48(1):10-18.
  • 5Platt J.A resource-allocating network for function interpolation[J].Neural Computation,1991,2(3):213-225.
  • 6Foster W R.Collopy F.Ungar L H.Neural network forecasting of short noisy time series[J].Computers Chem Enging,1992,16(4):293-297.
  • 7M W Garret ,W Willinger.Analysis,modeling and generation of self-similar VBR video traffic[J].ACM SIGCOMM Computer Communication Review,1994,24(4):269-280.
  • 8M Grossglauser,J C Bolot.On the relevance of long-range dependence in network traffic[J].IEEE/ACM Trans Networking,1999,7(5):629-640.
  • 9Vapnik V.The Nature of Statistical Learning Theory[M].New York:Springer Verlag,1995.5-13.
  • 10Simon Haykin,Neural Networks:A Comprehensive Foundation (second Edition)[M].Beijing:Tinghua University Press,2001,ISBN 7-302-04936-X/TP·2778.318-350.

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光电子．激光
2004年 第7期

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