Short-term Prediction of Traffic Rate Interval Router Using Dynamic Synapse Neural Network

Prediction is an important issue in many dynamical systems and is vital for effective management and control of plants. An important process which has recently derived much attention is the congestion control problem. Prediction of different traffic parameters can help in managing a congestion in a computer network. In this thesis, using real data for from the router between Iran Telecommunication Research Center and data Data company during December, January, February and March 2007, the router interval traffic rates are analyzed. Also, a comparative study is performed using the different methods employed and prediction results are provided to show the effectiveness of the predictions.