| Summary: | This paper presents an approach for the predictive analysis of teletraffic performance indices through derived analytical and regression structures based on Artificial Intelligence. The systematization of synthesis, testing, and verification processes for simulation-modeled teletraffic ICT infrastructure with queue service organization was carried out. The forecast models for the selected system throughput and system response time indices against the specific complex indicator Service Demand were obtained. Polynomial regression models based on the Coefficient of determination R were achieved. In the course of procedural teletraffic forecasting, we used Feed-Forward Neural Networks (FFNNs), Generalized Regression Neural Networks (GRNNs), and Cascade-Forward Neural Networks. The selection of neural models was performed as the functional minimization of the Mean-Squared Error (MSE) and Mean Absolute Error (MAE).
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