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Prediction of MPEG Video Source Traffic Using BiLinear Recurrent Neural Networks

  • Dong-Chul Park
  • Chung Nguyen Tran
  • Young-Soo Song
  • Yunsik Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4099)

Abstract

A prediction scheme for the MPEG video traffic in ATM networks using a BiLinear Recurrent Neural Network (BLRNN) is proposed in this paper. Since the BLRNN is based on the bilinear polynomial, it has been successfully used in modeling highly nonlinear systems with time-series characteristics, and the BLRNN can be a natural choice in predicting the MPEG video traffic with a bursty nature in the ATM networks. The proposed BLRNN-based predictor is applied to MPEG-1 and MPEG-4 video traffic data. The performance of the proposed BLRNN-based predictor is evaluated and compared with the conventional MultiLayer Perceptron Type Neural Network (MLPNN)-based predictor. When compared with the MLPNN-based predictor, the proposed BLRNN-based predictor shows 27%-51% improvement in terms of the Relative Mean Square Error (RMSE) criterion.

Keywords

Recurrent Neural Network Frame Size Bandwidth Allocation Video Source Relative Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dong-Chul Park
    • 1
  • Chung Nguyen Tran
    • 1
  • Young-Soo Song
    • 1
  • Yunsik Lee
    • 2
  1. 1.ICRL, Dept. of Information EngineeringMyong Ji UniversityKorea
  2. 2.SoC Research CenterKorea Electronics Tech. Inst.SeongnamKorea

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