DEVELOPMENT OF A SYSTEM FOR MONITORING THE PROCESS OF STUDYING POPULATION PROBLEMS BASED ON FUZZY LOGICAL ALGORITHMS
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Abstract
This paper presents a theoretical framework for a monitoring system that leverages fuzzy logic algorithms to study and analyze population issues. Population-related challenges – such as demographic shifts, aging, overpopulation, and socio-economic well-being – often involve complex, uncertain data and vague categorizations that are not well handled by traditional crisp analytic methods. Fuzzy logic, introduced by Zadeh, offers a means to model imprecision and degrees of truth, making it suitable for reasoning with ambiguous and incomplete information. In this work, we explore the application of fuzzy inference systems as the core of a population issue monitoring platform. We discuss the challenges in monitoring population issues (e.g., incomplete data, multi-dimensional indicators, and arbitrary thresholds) and justify the suitability of fuzzy logic for handling the inherent uncertainties in demographic and social data.
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References
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