Mathematical Foundations of Probabilistic Estimation in Fuzzy Bayesian Belief Networks

Parasyuk, I. M.; Verovka, O. V.

doi:10.1007/s10559-016-9816-3

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“…2. In [21] Bayesian Belief Networks are used as a mathematical tool, which have shown themselves well in modeling complex systems with uncertainties.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Influence of Environmental Factors and Cognitive Parameters on the Decision-Making Process in Human-Machine Systems of Critical Application

Perederyi,

Borchik,

Zosimov

et al. 2024

RIC

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Context. A feature of human-machine systems of critical application operating in real time is that they include as elements both technical systems and people interacting with these systems. At the same time, the main difficulties are associated not only with the improvement of hardware and software, but also with the insufficient development of methods for reliably predicting the impact of the production environment on the human factor and, as a result, on the relevance of decisions made by decision makers. As a result, the task of developing methods for determining the mutual influence of environmental factors and cognitive parameters of decision makers on the decision-making process becomes very relevant. Objective. The aim of the work is to propose methodological foundations for the development and study of fuzzy hierarchical relational cognitive models to determine the influence of environmental factors and cognitive parameters of decision makers on the DMP. Method. When building FHRCM methods of “soft computing”, methodologies of cognitive and fuzzy cognitive modeling were used, providing an acceptable formalization uncertainty of mutual influence of factors on the DMP. Results. A fuzzy cognitive model based on a fuzzy Bayesian belief network has been developed, which makes it possible to draw a connection between qualitative and quantitative assessments of mutually influencing factors on the DMP. The proposed model makes it possible to probabilistically predict the influence of factors and choose rational ways of their interaction in the DMP. Conclusions. The results of the experiments make it possible to recommend using the developed model, which takes into account the mutual influence of factors of various nature, including cognitive ones, in the DMP in order to improve the efficiency of HMSCA management as a whole.

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“…2. In [21] Bayesian Belief Networks are used as a mathematical tool, which have shown themselves well in modeling complex systems with uncertainties.…”

Section: Methodsmentioning

confidence: 99%