Abstract—In earlier papers, the authors identified critical parameters to be used in any effective identification of aircraft vortex encounters. Various techniques of pure fuzzy logic and hybrid soft-computing approaches were used to model and successfully classify vortex encounters. In this paper, the authors consider pure neural networks models having different architectures to identify aircraft encounters of wing-tip vortices. The automatic identification of airplane vortex encounters using neural networks gives excellent accuracy when compared with manual approaches. The highest accuracies are obtained by probabilistic neural networks. They are about 93%, 73% and 83% for the overall training, the overall testing and the overall average, respectively. The achieved results confirm the effectiveness of some neural network techniques and the choice of the critical parameters to automatically identify wing-tip vortices.
Index Terms—Wing tip vortices, vortex encounters, neural networks (NN), flight data records.
The authors are with the Dubai Men’s College, Higher Colleges of Technology, Dubai, UAE (e-mail: aziz.almahadin@hct.ac.ae, faouzi.bouslama@hct.ac.ae).
Cite: Aziz Al-Mahadin and Faouzi Bouslama, "Recognition of Airplane Wing-Tip Vortices Encounters Using Neural Networks," International Journal of Machine Learning and Computing vol. 9, no. 2, pp. 115-120, 2019.