Fuzzy clustering is well known as a robust and efficient way to reduce computation cost to obtain the better results. In the literature, many robust fuzzy clustering models have been presented such as Fuzzy C-Mean (FCM) and Possibilistic C-Mean (PCM), where these methods are Type-I Fuzzy clustering. Type-II Fuzzy sets, on the other hand, can provide better performance than Type-I Fuzzy sets, es...

Linear equality systems with fuzzy parameters and crisp variables defined by the extension principle are called possibilistic linear equality systems. The study focuses on the problem of stability (with respect to perturbations of fuzzy parameters) of the solution in these systems.

In 2004 Fullér and Majlender introduced the notion of covariance between fuzzy numbers by their joint possibility distribution to measure the degree to which they interact. Based on this approach, in this paper we will present the concept of possibilistic correlation representing an average degree of interaction between marginal distributions of a joint possibility distribution as compared to t...

Linear equality systems with fuzzy parameters and crisp variables defined by the Zadeh’s extension principle are called possibilistic linear equality systems. This study focuses on the problem of stability (with respect to small changes in the membership function of fuzzy parameters) of the solution in these systems.

In 2001 we introduced the notions of possibilistic mean value and variance of fuzzy numbers. In this paper we list some works that use these notions. We shall mention some application areas as well. 1 Possibilistic mean value, variance, covariance and correlation A fuzzy number A is a fuzzy set R with a normal, fuzzy convex and continuous membership function of bounded support. The family of fu...

We revisit Zadeh's notion of "evidence the second kind" and show that it provides foundation for a general theory epistemic random fuzzy sets, which generalizes both Dempster-Shafer belief functions possibility theory. In this perspective, deals with generated by while induced sets. The more allows us to represent combine evidence is uncertain fuzzy. demonstrate application formalism statistica...

Human reasoning is characterized by a degree of fuzziness and uncertainty. In the present paper we develop a fuzzy model for a better description of the reasoning process and we use the fuzzy systems’ total possibilistic uncertainty as well as the classical Shannon’s entropy (properly modified for use in fuzzy environments) in measuring the individuals’ reasoning skills. Classroom experiments a...

we present a new model and a new approach for solving fuzzylinear programming (flp) problems with various utilities for the satisfactionof the fuzzy constraints. the model, constructed as a multi-objective linearprogramming problem, provides flexibility for the decision maker (dm), andallows for the assignment of distinct weights to the constraints and the objectivefunction. the desired solutio...

The interpretation of membership functions of fuzzy sets as statistical likelihood functions leads to a probabilistic-possibilistic hierarchical description of uncertain knowledge. The fundamental advantage of the resulting fuzzy probabilities with respect to imprecise probabilities is the ability of using all the information provided by the data. This paper studies the possibility of using fuz...