The current problem of constructing a new artificial neural network adapted to the limited resources of onboard computing systems of unmanned aerial vehicles is investigated. The purpose of the study is to develop an artificial neural network architecture that uses the ideas of group consideration of Ivakhnenko's arguments and the Kolmogorov-Arnold method of representing a function of many variables by a composition of functions of lesser complexity. Instead of a difficult-to-implement activation function, the sum of the elements of the Kolmogorov–Gabor polynomial is used here. The result is the overall architecture of the hybrid network and the individual neuron within it. To train such a network, a combination of limited iteration and backpropagation methods is used. The adjustment algorithm is implemented in relation to the problem of binary classification of aircraft by invariant moments of their silhouettes. A comparison with the results of solving a similar problem based on artificial neural networks with activation functions such as sigmoid and “s-parabola” showed their comparability in quality.
At the Intelligent Control Laboratory of the Program Systems Institute of the RAS website: https://icontrol.psiras.ru/publications/
Math-Net.Ru: https://www.mathnet.ru/eng/iipr660
Khachumov M. V., Emelyanova Yu. G., Astrakhantseva A. A. Hybrid artificial neural network architecture and its application in pattern recognition tasks // Artificial Intelligence and Decision Making, no. 1, 2026, pp. 68–75.