A. Doulamis, N. Doulamis and S. Kollias 
An Adaptable Neural Network Model for Recursive Nonlinear Traffic Prediction and Modelling of MPEG Video Sources 
IEEE Transactions on Neural Networks, vol. 24(1), 2003, pp. 150166. 
ABSTRACT

Multimedia services and especially digital video is expected to be the major traffic component transmitted over communication networks [such as internet protocol (IP)based networks]. For this reason, traffic characterization and modeling of such services are required for an efficient network operation. The generated models can be used as traffic rate predictors, during the network operation phase (online traffic modeling), or as video generators for estimating the network resources, during the network design phase (offline traffic modeling). In this paper, an adaptable neuralnetwork architecture is proposed covering both cases. The scheme is based on an efficient recursive weight estimation algorithm, which adapts the network response to current conditions. In particular, the algorithm updates the network weights so that 1) the network output, after the adaptation, is approximately equal to current bit rates (current traffic statistics) and 2) a minimal degradation over the obtained network knowledge is provided. It can be shown that the proposed adaptable neuralnetwork architecture simulates a recursive nonlinear autoregressive model (RNAR) similar to the notation used in the linear case. The algorithm presents low computational complexity and high efficiency in tracking traffic rates in contrast to conventional retraining schemes. Furthermore, for the problem of offline traffic modeling, a novel correlation mechanism is proposed for capturing the burstness of the actual MPEG video traffic. The performance of the model is evaluated using several reallife MPEG coded video sources of long duration and compared with other linear/nonlinear techniques used for both cases. The results indicate that the proposed adaptable neuralnetwork architecture presents better performance than other examined techniques.

01 January , 2003 
A. Doulamis, N. Doulamis and S. Kollias, "An Adaptable Neural Network Model for Recursive Nonlinear Traffic Prediction and Modelling of MPEG Video Sources", IEEE Transactions on Neural Networks, vol. 24(1), 2003, pp. 150166. 
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