Synthesizing of models for identification of teletraffic Markov chains by artificial neural networks and decision tree method

Imitation modelling processes of telegraphic systems on the Markov chains with unlimited and limited queues were made. For this purpose, the Java modeling tool simulation environment is used. With a fixed number of client stations and a number of system users, data are accumulated about the telegraphic system parameters as: customer ID, arrival time, server ID and exit system. Artificial neural networks (ANN) with backpropagation algorithm and decision tree (DT) method for identification of the studied Markov chains in MATLAB were applied. Training of the structural identification models to determine of the membership of the obtained parameters in telegraphic simulation to both unlimited and limited systems was carried out. The results of the training and synthesis of ANN and DT models are presented. Sufficient results have been obtained for telegraphic identification confirming the successful application of the proposed synthesized classification models, approximately 91% for DT and 99.2% for ANN.