Artificial Neural Network for Pre-Simulation Training of Air Traffic Controller

Tetiana Shmelova (National Aviation University, Ukraine), Yuliya Sikirda (National Aviation University, Ukraine) and Togrul Rauf Oglu Jafarzade (National Aviation Academy, Azerbaijan)
Copyright: © 2019 |Pages: 51
EISBN13: 9781522595830|DOI: 10.4018/978-1-5225-7588-7.ch002
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In this chapter, the four layers neural network model for evaluating correctness and timeliness of decision making by the specialist of air traffic services during the pre-simulation training has been presented. The first layer (input) includes exercises that cadet/listener performs to solve a potential conflict situation; the second layer (hidden) depends physiological characteristics of cadet/listener; the third layer (hidden) takes into account the complexity of the exercise depending on the number of potential conflict situations; the fourth layer (output) is assessment of cadet/listener during performance of exercise. Neural network model also has additional inputs (bias) that including restrictions on calculating parameters. The program “Fusion” of visualization of the state of execution of an exercise by a cadet/listener has been developed. Three types of simulation training exercises for CTR (control zone), TMA (terminal control area), and CTA (control area) with different complexity have been analyzed.
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