In the approximate fuzzy reasoning the covering over of fuzzy rule base input and rule premise of a rule determines the importance of that fuzzy rule and the rule output as well. An axiom system has been created, describing the relationship between the fuzzy rule base system, rule input and rule output. By using distance-based operators a novel reasoning method appears by the compositional rule...

In case of fuzzy reasoning in sparse fuzzy rule bases, the question of selecting the suitable fuzzy similarity measure is essential. The rule antecedents of the sparse fuzzy rule bases are not fully covering the input universe therefore fuzzy reasoning methods applied for sparse fuzzy rule bases requires similarity measures able to distinguish the similarity of non-overlapping fuzzy sets, too. ...

Genetic fuzzy rule selection is an effective approach to the design of accurate and interpretable fuzzy rule-based classifiers. It tries to minimize the complexity of fuzzy rule-based classifiers while maximizing their accuracy by selecting only a small number of fuzzy rules from a large number of candidate rules. One important issue in genetic fuzzy rule selection is the prescreening of candid...

Leibniz’s principles made for an elegant and coherent philosophy. In part metaphysical, in part methodological, they addressed fundamental questions in the treatment of symmetry, in the relationship of physics to mathematics, in logic that are if anything even more pressing today than they were in Leibniz’s time. As I shall read them, they also expressed a distinctive and uncompromising form of...

predicting different behaviors in computer networks is the subject of many data mining researches. providing a balanced intrusion detection system (ids) that directly addresses the trade-off between the ability to detect new attack types and providing low false detection rate is a fundamental challenge. many of the proposed methods perform well in one of the two aspects, and concentrate on a su...

in this paper, a  fuzzy numerical procedure for solving fuzzy linear volterra integro-differential equations of the second kind under strong  generalized differentiability is designed. unlike the existing numerical methods, we do not replace the original fuzzy equation by a $2times 2$ system ofcrisp equations, that is the main difference between our method  and other numerical methods.error ana...