Geodesic
Reducing type I errors in aircraft contour recognition using collective intelligence of unmanned aerial vehicles
News of the Kabardin-Balkar scientific center of RAS, no. 6-3 (2018), pp. 70-82.

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This study work presents the problem of reducing type I errors in aircraft contour recognition and offers a solution for it. The study models a distributed intelligence of a group of unmanned aerial vehicles simulated in their onboard computers using pretrained neural networks. It provides a definition of an evolutionary solution matching method with a theoretical description based on genetic algorithms, Condorcet's jury theorem, and the Rasch model. The study demonstrates conditions significantly reducing the probability of wrong decisions. It offers and tests a two-level hierarchy of collective intelligence based on collective application of evolutionary matching using neural networks as intelligent agents.
Mots-clés : UAV
Keywords: evolutionary matching method, type I errors, neural networks, onboard computer, distributed computing, hierarchy. 
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V. I. Protasov; R. O. Mirahmedov; Z. E. Potapova; M. M. Sharnin; A. V. Sharonov. Reducing type I errors in aircraft contour recognition using collective intelligence of unmanned aerial vehicles. News of the Kabardin-Balkar scientific center of RAS, no. 6-3 (2018), pp. 70-82. http://geodesic.mathdoc.fr/item/IZKAB_2018_6-3_a6/

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