ISSN:
1573-7497
Keywords:
air traffic control
;
collision avoidance
;
neural networks
;
genetic algorithms
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract As air traffic keeps increasing, many research programs focus on collision avoidance techniques. For short or medium term avoidance, new headings have to be computed almost on the spot, and feed forward neural nets are susceptible to find solutions in a much shorter amount of time than classical avoidance algorithms (A *, stochastic optimization, etc.) In this article, we show that a neural network can be built with unsupervised learning to compute nearly optimal trajectories to solve two aircraft conflicts with the highest reliability, while computing headings in a few milliseconds.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1026507809196
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