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J.P. Kharat
Electronics at DKTE Ichalkaranji
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction J.P. Kharat
International Journal of Advances in Applied Sciences Vol 6, No 4: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.998 KB) | DOI: 10.11591/ijaas.v6.i4.pp283-292

Abstract

Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic predictions evaluated by different types of neural network architectures and compares the performance of the same in terms of mean square error for the same video frames. For that three types of neural architectures are used namely Feed forward, Cascaded Feed forward and Time Delay Neural Network. The results show that cascade feed forward network produces minimum error as compared to other networks. This paper also compares the results of traditional prediction method of averaging of frames for future frame prediction with neural based methods. The experimental results show that nonlinear prediction based on NNs is better suited for traffic prediction purposes than linear forecasting models.
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