POSITIVE IMPLICATIVE (∈,∈ ∨q)-FUZZY IDEALS ((¯∈, ¯∈ ∨ ¯q)-FUZZY IDEALS, FUZZY IDEALS WITH THRESHOLDS) OF BCK-ALGEBRAS

Abstract —Embedding non-vectorial data into a normed vec- torial space is very common in machine learning, aiming to perform tasks such classification, regression, clustering and so

Moments and semi-moments of a (random or fuzzy) variable, defined by means of crisp or fuzzy measure, are used in optimization models for the first approach ([10],[13], [5],

In this case a task of definition optimum allocation of the service centres may be transformed into the task of definition of fuzzy antibases of fuzzy graph.. The

It is shown that proto-fuzzy concepts are closely related to crisply generated fuzzy concepts in the sense that the mappings involved in the definitions coincide for crisp subsets

We prove the existence a equilibrium pair of abstract fuzzy economies in several cases (economies with U -majorized preference correspon- dences and upper semicontinuous

Pu and Liu [26] introduced the concept of quasi-coincidence of a fuzzy point with a fuzzy set, plays a fundamental role to make some different types of fuzzy subgroups, called

In this section, we introduce the concepts of fuzzifying ideal, commutative fuzzifying ideal, t-implication-based fuzzy ideal and commutative t-implication- based fuzzy ideal

Таким чином, якщо випадання числа 5 назвати вдалою подією, то число вдалих подій при кожних шести підкиданнях кубика буде близьким до 1, тобто, ймовірність вдалої