V.V. Isaev1, L.E. Mistrov2

1 JSC RIE “PROTEK” (Voronezh, Russia)

2 Central Branch of the Russian State Unitary Enterprise named after V.M. Lebedev (Voronezh, Russia)

1 visaev@protek-vrn.ru; 2 mistrov_le@mail.ru

Problem statement The development of intelligent training systems (ITS) is the basis for training specialists in the management of the use of radio-electronic objects (REOs), which is associated with the presence of various types of uncertainties, the causes of which are the incompleteness, inaccuracy, unreliability, and incorrectness of the information used, which makes it necessary to synthesize ITS under conditions of risk, uncertainty, and fuzzy (in the sense of Zadeh) information. Therefore, decisions on the choice of the most preferable ITS option must be made based on the structuring and resolution of various types of uncertainty.

Purpose. Develop methodological approaches to resolving various types of uncertainty in order to substantiate the principles and rules for selecting the preferred ITS option.

Results. It is shown that the basis of ITS synthesis is the substantiation of the maximum utility principle based on the parrying of various types of uncertainties and reducing the synthesis problem to a mathematical programming problem. The resolution of uncertainties in solving the ITS synthesis problem under various conditions has been carried out. A method has been developed for resolving various types of uncertainties, which ensures the solution of the ITS synthesis problem and the substantiation of its preferred variants based on a set of uncertainty factors.

Practical significance. The proposed decision-making framework for the synthesis of IT systems allows for the resolution of various types of uncertainties in the technical specifications and justifies the selection of the preferred option, taking into account the technical feasibility and conflicting requirements for the components.

Isaev V.V., Mistrov L.E. Fundamentals of decision-making to resolve uncertainties in the problem of synthesizing an intelligent trai-ning systems // Radiotekhnika. 2026. V. 90. № 7. P. 119−130. DOI: https://doi.org/10.18127/j00338486-202607-19

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