An L-fuzzy context is a triple consisting of a set of objects, a set of attributes and an L-fuzzy binary relation between them. An l-cut is a classical context over the same sets with relation as a set of all object attribute pairs, which fuzzy relation assigns truth degree geater or equal than l. Proto-fuzzy concept is a triple made of a set of objects and a set of attributes, which form a con...

In this study a new approach to rank exponential fuzzy numbers using  -cuts is established. The metric distance of the interval numbers is extended to exponential fuzzy numbers. By using the ranking of exponential fuzzy numbers and using  -cuts the critical path of a project network is solved and illustrated by numerical examples. Keywords: Exponential Fuzzy Numbers,  -cuts, Metric Dista...

As a generalization of the L-fuzzy contexts, we propose the study of the L-fuzzy hypercontexts where the relation R between the objects X and the attributes Y takes as values other L-fuzzy relations. In this work, we propose the study of these structures using OWA operators in different situations. Finally, the practical case that has motivated this paper is analyzed.

In a previous work, we showed that artificial neural networks (ANNs) could learn the criteria for comparing fuzzy numbers of a real decision maker. A multilayer feed-forward ANN and the backpropagation algorithm, and trapezoidal fuzzy numbers were considered. The criteria of three people were learnt with an ANN. The trained ANN is considered as a personal method of the decision-maker to compare...

Ranking fuzzy numbers plays a very important role in decision making and some other fuzzy application systems. Many different methods have been proposed to deal with ranking fuzzy numbers. Constructing ranking indexes based on the centroid of fuzzy numbers is an important case. But some weaknesses are found in these indexes. The purpose of this paper is to give a new ranking index to rank vario...

Given an implication function I defined on the finite chain L = {0, ..., n}, a method for extending I to the set of discrete fuzzy numbers whose support is a set of consecutive natural numbers contained in L (denoted by A1 ) is given. The resulting extension is in fact a fuzzy implication on A1 preserving some boundary properties. Moreover, if the initial implication I is an S, QL or D-implicat...