Denys Katielnikov

We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. The proposed influence comparison method simplifies expert elicitation by reducing the cognitive load of direct weight estimation, while the genetic algorithm ensures alignment of forecasts with observed or expert-derived data. A forecasting algorithm based on incremental changes in concept levels enhances the sensitivity of the output variable to factor variations. To illustrate the applicability of the framework, we construct a decision-support model for predicting weight-loss success under diverse psychological, behavioral, and environmental conditions. Simulation results demonstrate how factor ranking, scenario modeling, and paired influence analysis provide actionable insights for decision-making. Beyond the weight-loss domain, the approach is generalizable to a wide range of knowledge-based systems requiring robust integration of expert judgment, fuzzy reasoning, and evolutionary optimization.

This paper proposes an intelligent model for controlling the reliability and safety of urban transport systems, integrating fuzzy cognitive maps (FCMs) with the Bellman-Zadeh decision-making principle. This approach enables the consideration of complex interactions between evaluation criteria and controllable variables, which are characteristic of modern urban environments. To harmonise the modelling outcomes, a membership function to the ideal solution is introduced, allowing for the aggregation of the criteria vector through the intersection of corresponding fuzzy sets. A generalised algorithm is developed for forecasting reliability and safety parameters under the influence of multiple factors, facilitating multi-criteria selection of alternatives in dynamic and uncertain conditions. Special attention is given to the model’s applicability in road safety audits, where both technical and behavioural risk factors must be considered. The proposed framework supports scenario analysis, enabling the simulation of various event developments, the assessment of their implications for transport safety, and the formulation of adaptive response strategies. The integration of FCMs with the Bellman-Zadeh principle formalises the evaluation of safety scenarios, ranks critical factors, and supports decision-making for the optimisation of transport infrastructure. The approach can be adapted to various types of transport systems and utilised to enhance risk management, environmental safety, and strategic planning in urban contexts.

The Russian–Ukrainian conflict is considered as a dynamic system, whose variables are factors affecting the losses of the Russian army and the threat of the use of nuclear weapons. A fuzzy cognitive map (FCM) is used for modeling, that is, a directed graph whose vertices are model variables, and the weights of arcs are the degrees of positive and negative influences of variables on each other. The following factors influencing the losses of the Russian army and the threat of a nuclear strike were selected: resistance of the Ukrainian army, support of Ukraine with weapons, economic sanctions against Russia, opposition to the Russian government and its self-preservation instinct. The degrees of the influence of factors on each other and on the possibility of using nuclear weapons are evaluated by experts using fuzzy terms, which correspond to numeric values. To adjust the FCM, a genetic algorithm is used to select the degrees of influence of factors that minimize the discrepancy between the simulation results and expert estimations. The obtained FCM is used for scenario modeling of the conflict according to the “what if” scheme and ranking of factors according to their degree of influence on the level of nuclear threat.

Fuzzy sets membership functions integrated with logistic map as the chaos generator were used to create reliability bifurcations diagrams of the system with redundancy of the components. This paper shows that increasing in the number of redundant components results in a postponement of the moment of the first bifurcation which is considered as most contributing to the loss of the reliability. The increasing of redundancy also provides the shrinkage of the oscillation orbit of the level of the system’s membership to reliable state. The paper includes the problem statement of redundancy optimization under conditions of chaotic behavior of influencing parameters and genetic algorithm of this problem solving. The paper shows the possibility of chaos-tolerant systems design with the required level of reliability.