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Collision avoidance using neural networks learned by genetic algorithms

Abstract : As Air Traffic keeps increasing, many research programs focus on collision avoidance techniques. In this paper, a neural netwok learned by genetic algorithm is introduced to solve conflicts between two aircrafts. The learned NN is then tested on different conflicts and compared to the optimal solution. Results are very promising.
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https://hal-enac.archives-ouvertes.fr/hal-00937688
Contributor : Laurence Porte <>
Submitted on : Friday, April 25, 2014 - 4:07:59 PM
Last modification on : Tuesday, June 2, 2020 - 12:10:09 PM
Long-term archiving on: : Friday, July 25, 2014 - 10:41:19 AM

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Nicolas Durand, Jean-Marc Alliot, Joseph Noailles. Collision avoidance using neural networks learned by genetic algorithms. IEA-AEI 1996, 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert systems, Jun 1996, Nagoya, Japan. pp xxxx. ⟨hal-00937688⟩

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