Open AccessComparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction
Author(s) -
Jayashree Kharat
Publication year - 2017
Publication title -
international journal of advances in applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2722-2594
pISSN - 2252-8814
DOI - 10.11591/ijaas.v6.i4.pp283-292
Subject(s) - artificial neural network , computer science , frame (networking) , nonlinear system , mean squared error , key (lock) , mean squared prediction error , real time computing , cascade , artificial intelligence , algorithm , engineering , computer network , statistics , mathematics , physics , computer security , quantum mechanics , chemical engineering
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.