Taras Hurskyi

TL;DR: The proposed algorithm improves the processing speed of information in special-purpose geoinformation systems from 16 to 20 % depending on the amount of information about the monitoring object, which allows to increase the efficiency of geo Information systems due to complex processing of geospatial data circulating in it.

TL;DR: Development of the proposed algorithm was predetermined by the need to train artificial neural networks for intelligent decision support systems in order to process more information given the unambiguity of decisions being made and the research results revealed that the specified training algorithm provides on average 16–23 % higher the efficiency of trainingificial neural networks training.

TL;DR: The article develops an approach to the selection of indicators for assessing the effectiveness of the use of electronic warfare devices, and uses a combination of system-resource approach and general scientific methods of analysis and synthesis to calculate indicators on the basis of the multilevel description of the composition of the devices of electronic Warfare.

TL;DR: An improved method for finding solutions for neuro-fuzzy expert systems that reduces the computational complexity of decision-making and does not accumulate errors in the training of artificial neural networks as a result of processing the information coming to the input of artificial Neural networks.

TL;DR: In this paper , a method for the distribution of forces and devices of communication of groupings of troops (forces) in operations was developed, taking into account the type of uncertainty regarding the operational situation in the operational space.