Fuzzy Logic and Control Systems
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The results demonstrate that FAMs offer improved flexibility, transparency, and accuracy compared to conventional approaches, and serve as an efficient methodology for addressing dynamic and uncertain problems across various domains, supporting better decision-making and intelligent system design.
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Fuzzy Associative Models (FAMs) are powerful tools for knowledge representation and reasoning in uncertain and complex environments. By integrating fuzzy logic with associative memory, FAMs provide an interpretable and robust framework for modeling nonlinear relationships and handling imprecise information. This study explores the capabilities of FAMs in analyzing complex real-world problems, including socio-economic and environmental issues. The model enables effective prediction, decision-making, and control by capturing uncertain relationships among variables through fuzzy associations. The results demonstrate that FAMs offer improved flexibility, transparency, and accuracy compared to conventional approaches. Therefore, FAMs serve as an efficient methodology for addressing dynamic and uncertain problems across various domains, supporting better decision-making and intelligent system design.
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@article{C2026Description,
title = {Description of Fuzzy Associative Memories Model (FAM) and Their Applications},
author = {Dr.Venkatesan C. and Vimala J.},
journal = {International Journal of Innovative Research in Advanced Engineering},
year = {2026},
doi = {10.26562/ijirae.2026.v1306.01},
url = {https://doi.org/10.26562/ijirae.2026.v1306.01}
}FAQ
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