Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Generator of Fuzzy Implications

Version 1 : Received: 30 October 2023 / Approved: 31 October 2023 / Online: 31 October 2023 (06:09:40 CET)

A peer-reviewed article of this Preprint also exists.

Daniilidou, A.; Konguetsof, A.; Souliotis, G.; Papadopoulos, B. Generator of Fuzzy Implications. Algorithms 2023, 16, 569. Daniilidou, A.; Konguetsof, A.; Souliotis, G.; Papadopoulos, B. Generator of Fuzzy Implications. Algorithms 2023, 16, 569.

Abstract

In this research paper an algorithm for the derivation of methods based on theorems and axioms of fuzzy logic is presented, analyzed and applied. This new proposed procedure generates fuzzy methods and evaluates the value of fuzzy propositions through fuzzy implication. The new implications of the family should satisfy a number of axioms. Also, we denote the conditions in order to satisfy the maximum number of axioms. Moreover, authors state and prove theorems focusing on fuzzy implication, observing the rule: the fuzzy function of fuzzy implication is strong and that lead to fuzzy negation. In addition, the optimal number of repetitions is calculated according to the expected truth value. The formulas produced are verified through the use of temperature and humidity values over a certain period of time and in a certain geographical area in Greece. The basic steps of the methodology are the fuzzification of the data given ​​using four membership degree functions and the application of the membership degrees of temperature and humidity values on a new type of fuzzy implication. Finally, we observed that the isosceles trapezium gave the best results and the type of fuzzy implication can be effectively applied.

Keywords

fuzzy logic; fuzzy implications; fuzzy negations; fuzzy disjunction; probor; t-conorm; temperature; humidity; triangular membership function; trapezium membership function

Subject

Computer Science and Mathematics, Applied Mathematics

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