H.A. Safa1, A.V. Kiselev2, V.Yu. Batumsky3
1, 2 Peoples' Friendship University of Russia named after Patrice Lumumba (Moscow, Russia)
3 Regional Public Organization for the Promotion of Activities in the Field of Arts «Union of Pop Artists» (Yuzhno-Sakhalinsk, Russia)

1 sa.programmer1@gmail.com, 2 kiselyov.av@gmail.com, 3 batumski@batumski.ru

In security systems and in crowded places, automated recognition of human intentions by their movements is one of the most important tasks. Traditionally, artificial neural networks are used to solve such problems. To recognize the type of human movements, a hybrid system has been proposed that combines two algorithms, namely neural network and evolutionary algorithms.

The aim of the research is to develop a neuroevolutionary algorithm that combines the theory of genetic algorithms and the theory of neural networks to recognize the nature of human movements.

An algorithm for optimizing neural network parameters has been described as a replacement for the error backpropagation procedure. A modified formal neuron and the structure of a direct-flow neural network have been proposed. The developed system allows you to determine the type of human movement with high accuracy.

The developed algorithm can be used to solve problems of forecasting, diagnosis and classification of human movements.

Safa H.A., Kiselev A.V., Batumsky V.Yu. Neuroevolutionary algorithm for classifying human intentions by his movements. Neurocomputers. 2025. V. 27. № 4. P. 45–51. DOI: https://doi.org/10.18127/j19998554-202504-04 (in Russian)

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