Intelligent air traffic controller simulation using artificial neural networks

This paper deals with Artificial Neural Networks as a basis for the automation of existing Air Traffic Control System (ATC). The air traffic control task involves huge complexity in terms of work load on air traffic controller. This task is having complex cognitive nature, with main objective is to maintain safe distance between two aircrafts during departure, landing and in middle air traffic. The approach used is back propagation network for the decision making. The approach used is gradient Descent. A simulator is design which simulates control of Air Traffic and Landing Clearance and departure by Artificial intelligence. Decisions can be given based on various controlling parameters. The intelligent decision making can be implemented by using “Back propagation Network” (BPN). The system simulates various parameters and the effect of these parameters on the output decision of BPN can be analyzed. The output decision will vary according to the updated flight record.