Prediction of MPEG Video Source Traffic Using BiLinear Recurrent Neural Networks
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.
KeywordsRecurrent Neural Network Frame Size Bandwidth Allocation Video Source Relative Mean Square Error
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