Toward Modeling Linguistic Fuzzy Spanning Trees Based on Hedge Algebra
DOI:
https://doi.org/10.4108/eetcasa.7337Keywords:
Linguistic Fuzzy Maximum Spanning Tree, Hedge Algebra, Fuzzy graph, Linguistic VariablesAbstract
This paper presents an innovative approach to modeling Linguistic Fuzzy Maximum Spanning Trees (L-FMSTs) using Hedge Algebra (HA). HA provides a robust framework for quantifying linguistic terms, which is essential for handling the vagueness inherent in natural language. By integrating HA with L-FMSTs, we aim to enhance the interpretability and performance of fuzzy systems in applications requiring complex decision-making and optimization.
References
[1] Nguyen Van Han and Phan Cong Vinh. Toward computing linguistic fuzzy graphs and applying to illegal immigration problem. EAI Endorsed Trans. Context-aware Syst. & Appl, 7(22):166361, 2020.
[2] Nguyen Cat Ho, Tran Thai Son, Tran Dinh Khang, and Le Xuan Viet. Fuzziness measure, quantified semantic mapping and interpolative method of approximate reasoning in medical expert systems. Journal of Computer Science and Cybernetics, 18(3):237–252, 2002.
[3] Nguyen Cat Ho and W.Wechler. Hedge algebras: An algebraic approach to structure of sets of linguistic truth values. Fuzzy Sets and Systems, 35(3):281–293, 1990.
[4] E. H. Mmdani. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, C-26(12):1182–1191, 1977.
[5] J.N. Mordeson and P.S. Nair. Fuzzy Graphs and Fuzzy Hypergraphs. Physica-Verlag, Heidelberg, 2000.
[6] John N. Mordeson and Sunil Mathew. Advanced Topics in Fuzzy Graph Theory. Springer Nature Switzeland AG, 2019.
[7] A Rosenfeld. Fuzzy graphs. Fuzzy Sets and Their Applications, pages 77–95, 1975.
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