Developing neuro-fuzzy hybrid networks to aid predicting abnormal behaviours of passengers and equipments inside an airplane
Ali H. Ali, Alex Tarter · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
The terrorist attack of 9/11 has revealed how vulnerable the civil aviation industry is from both security and safety points of view. Dealing with several aircrafts cruising in the sky of a specific region requires decision makers to have an automated system that can raise their situational awareness of how much a threat an aircraft presents. In this research, an in-flight array of sensors has been deployed in a simulated aircraft to extract knowledge-base information of how passengers and equipment behave in normal flighttime which has been used to train artificial neural networks to provide real-time streams of normal behaviours. Finally, a cascading of fuzzy logic networks is designed to measure the deviation of real-time data from the predicted ones. The results suggest that Neural-Fuzzy networks have a promising future to raise the awareness of decision makers about certain aviation situations.