Abstract: In this paper, the theory of T-norm and T-conorrn is reviewed and the T-norm, T-conorm and negation function are defined as a set of T-operators. Some typical T-operators and their mathematical properties are presented. Finally, the T-operators are extended to the conventional fuzzy reasoning methods which are based on the MIN and MAX operators. This extended fuzzy reasoning provides ...

The fuzzy min-max classiication network constitutes a promisimg pattern recognition approach that is based on hy-berbox fuzzy sets and can be incrementally trained requiring only one pass through the training set. The deenition and operation of the model considers only attributes assuming continuous values. Therefore, the application of the fuzzy min-max network to a problem with continous and ...

Fuzzy logics based on triangular norms and their corresponding conorms are investigated. An aarmative answer to the question whether in such logics a speciic level of satissability of a set of formulas can be characterized by the same level of satissability of its nite subsets is given. Tautologies, contradictions and contingencies with respect to such fuzzy logics are studied, in particular fo...

This paper focuses on the maximization of minimum completion time identical parallel processors. The objective this is to ensure fair distribution. Let a set jobs be assigned several problem shown as NP-hard. research work based essentially comparison proposed heuristics with others cited in literature review. Our are developed using randomization method and iterative utilization knapsack solve...

In this paper, first a simplified version of a general fuzzy automaton with a fixed c is defined and then a subepisode of a simplified version of a general fuzzy automaton with a constant c is defined and explained connected and strong connected them, and finally, a model of quasi-order hypergroup on general fuzzy automaton with constant c is presented and the relationships between them are rev...

The real life problems deal with imperfectly specified knowledge and some degree of imprecision, uncertainty or inconsistency is embedded in the problem specification. The well-founded theory of fuzzy sets is a special way to model the uncertainty. The rules in a fuzzy model contain a set of propositions, each of which restricts a fuzzy variable to a single fuzzy value by means of the predicate...

This paper presents a new decision tree learning algorithm, fuzzy min-max decision tree (FMMDT) based on fuzzy min-max neural networks. In contrast with traditional decision trees in which a single attribute is selected as the splitting test, the internal nodes of the proposed algorithm contain a fuzzy min-max neural network. In the proposed learning algorithm, the exibility inherent in the fuz...