Classification and Prediction of Interference Pathloss Measurements Inside B-757 Using Feed Forward Neural Networks (Prepared December 2005)

Neural network modeling is introduced in this paper to classify and predict interference pathloss measurements on a Boeing 757 airplane. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structure and the aircraft system-of-concern. Modeled results are compared with measured data and a plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft