Developing a set of models to support intelligent decision support systems
DOI:
https://doi.org/10.15587/1729-4061.2026.362504Keywords:
destabilizing factors, complex systems, efficiency of decision-making, modeling of complex systemsAbstract
Intelligent decision support systems are the object of the study. The problem addressed in the study is the improvement of the validity of the functioning of intelligent decision support systems. The hypothesis of the study is the possibility of increasing the efficiency of the functioning of intelligent decision support systems due to the development of a set of mathematical models of their functioning.
The originality of the study consists of:
– comprehensive assessment of the state of functioning of intelligent decision support systems due to multi-level assessment;
– modeling of possible states of functioning of intelligent decision support systems;
– reconfiguring the number of input parameters to model the functioning process of intelligent decision support systems due to the use of evolving artificial neural networks, which achieves an increase in the efficiency and reliability of the received decisions and evaluations;
– setting the number of input channels of destructive influence for their accurate assessment due to the use of queuing theory;
– setting the input parameters of the models by adjusting the parameters of the membership function of evolving artificial neural networks, which achieves an increase in the accuracy of modeling the state of functioning of intelligent decision support systems.
Modeling of the proposed set of mathematical models of the functioning of intelligent decision support systems was carried out. In the course of modeling, it was established that an average of up to 20% gain is ensured in the efficiency and reliability of calculations, while ensuring an average level of use of hardware resources
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Copyright (c) 2026 Hennadii Shapovalov, Vladyslav Shostak, Oleg Sova, Viktor Pokaliuk, Oleksandr Yefymenko, Elena Odarushchenko, Olesia Zhuk, Bohdan Molodetskyi, Yevhen Sudnikov, Roman Lazuta

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