FUZZY LOGIC FOR MANAGING COMPLEX UNCERTAINTY IN PREDICTIVE AND DECISION SUPPORT SYSTEMS
DOI:
https://doi.org/10.35631/JISTM.1144032Keywords:
Uncertainty, Fuzzy Decision Making, Fuzzy Prediction, Fuzzy Logic, Fuzzy Inference SystemAbstract
This paper addressed how fuzzy logic can represent and handle uncertainty across diverse prediction and decision-support applications. Six fuzzy logic systems were examined, covering case of diabetes risk prediction, personalized diet recommendation, rainfall prediction, supply-demand identification in online food delivery, educational decision support, and air quality assessment. For each case study, relevant characteristics were extracted, including uncertain input factors, membership function selection, fuzzy rule bases, inference process, and output interpretation. The analysis was conducted to identify recurring and specific patterns in the representation of uncertainty processing. The findings reveals that fuzzy systems across the six applications share a common reasoning structure that transforms imprecise input information into interpretable outputs through membership functions, rule-based reasoning, and fuzzy inference, while differing in the selection of uncertainty-related variables, membership function configurations, and rule structures according to case requirements. Based on the identified patterns, a generalized uncertainty-aware fuzzy inference framework is projected to illustrate the systematic progression of fuzzy reasoning to interpretable prediction, recommendation, assessment, or decision-support outputs. The findings also demonstrate the adaptability of fuzzy logic in representing gradual and imprecise information across diverse application domains. The proposed framework provides a conceptual foundation for the development of future fuzzy inference systems and offers practical guidance for researchers and practitioners developing for uncertainty-based applications in the future endeavours.
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