AFSM: Advanced Fuzzy Synthetic Matrices
Keywords:
Decision-making, Fuzzy logic, Fuzzy synthetic matrices, Matrix theory, Soft computing, Uncertainty modellingAbstract
Fuzzy Synthetic Matrices (FSMs) have become a valuable analytical tool for modelling complex systems characterized by uncertainty, imprecision, and subjectivity. This paper explores recent advancements in FSM theory and applications, focusing on enhancing their structural adaptability, computational efficiency, and integration capabilities with other fuzzy and soft computing techniques. We propose an extended FSM framework that introduces dynamic weighting mechanisms, improved aggregation operators, and multi-level matrix configurations to better capture nuanced relationships among variables. The proposed methodology is evaluated through simulations and real-world case studies in decision-making, pattern recognition, and risk analysis. Results indicate improved interpretability, scalability, and robustness compared to conventional FSM models. These advancements highlight FSMs as a promising direction in the ongoing evolution of fuzzy systems, with significant potential across interdisciplinary domains.
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