Analysing of Flying Conditions of Aircrafts Using Artificial Neural Networks

In spite of advanced technology, the commercial aircraft's accidents are increasing year by year. Therefore, it is very important to analyse and predict flying conditions of aircrafts such as time to destination, distance to destination with outside temperature, altitude, ground speed and head wind. In this work, experimental measurements are taken from the aircraft during flying. Neural network based predictors are also designed to analyse destination time and destination distance for secure travelling conditions of passengers. Two type neural networks are used as predictor, that is, Back Propagation Neural Network (BPNN) and Radial Bases Neural Network (RBNN). The results show that RBNN has superior performance to adapt the parameters of the aircraft.