This research work is an extension of a previous work in which the authors created an innovative family of fuzzy implication using as t-conorm probor, as fuzzy negation the 1-x and the type of Newton's binomial. The purpose of this paper is to compare the type of fuzzy implication probor with three other constructed fuzzy implications produced by max, Einstein and Lukasiewicz t-conorms maintaining in each case the same negation. T-conorms perform the fuzzy logical operation "or" of the corresponding fuzzy sets. The main methods used for the comparisons are the basic axioms of fuzzy logic. The authors performed six combinations of t-conorm comparisons, in order to find the rank order of the five fuzzy implications. Although the general type of fuzzy implication probor came out smaller than Hamacher, Einstein and Lukasiewicz and the type of Einstein smaller than Lukasiewicz authors make extensive tests using four different membership functions and different values to the parameter m so as to examine which out of the four fuzzy implications receive the best results. The application of isosceles trapezium to the fuzzy implications probor and Einstein give the best values (most values greater than or equal to 0.9 and equal to 1).

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.