Dubois and Prade introduced the mean value of a fuzzy number as a closed interval bounded by the expectations calculated from its upper and lower distribution functions. In this paper introducing the notations of lower possibilistic and upper possibilistic mean values we definine the interval-valued possibilistic mean and investigate its relationship to the interval-valued probabilistic mean. W...

This paper is an attempt to cast both uncertainty and time in a logical framework. It generalizes possibilistic logic, previously developed by the authors, where each classical formula is associated with a weight which obeys the laws of possibility theory. In the possibilistic temporal logic we present here, each formula is associated with a time set (a fuzzy set in the more general case) which...

The ever-increasing concerns of the growth in volume waste tires and new strict government legislations to reduce environmental impact end-of-life (EOL) have increased interest among companies design a sustainable efficient closed-loop supply-chain (CLSC) network. In real world, CLSC network is subject variety uncertainties, such as random fuzzy (epistemic) uncertainties. Designing reliable env...

Providing an efficient production plan that integrates the procurement and distribution plans into a unified framework is critical to achieving competitive advantage. In this paper,we consider a supply chainmaster planningmodel consisting ofmultiple suppliers, one manufacturer andmultiple distribution centers.Wefirst propose a newmulti-objective possibilisticmixed integer linear programming mod...

AbstructTraditionally, prototype-based fuzzy clustering algorithms such as the Fuzzy C Means (FCM) algorithm have been used to find “compact” or “filled” clusters. Recently, there have been attempts to generalize such algorithms to the case of hollow or “shell-like” clusters, i.e., clusters that lie in subspaces of feature space. The shell clustering approach provides a powerful means to solve ...

Most of the real life classification problems have ill defined, imprecise or fuzzy class boundaries. Feedforward neural networks with conventional backpropagat ion learning algorithm are not tailored to these kinds of classafication problems. Hence, an this paper, feedforward neural networks, that use fuzzy objective functions in the backpropagation learning algorithm, are investigated. A learn...

Statistical distribution of chemical fingerprints p. 11 Fuzzy transforms and their applications to image compression p. 19 Development of neuro-fuzzy system for image mining p. 32 Reinforcement distribution in continuous state action space fuzzy Q-learning : a novel approach p. 40 A possibilistic approach to combinatorial optimization problems on fuzzy-valued matroids p. 46 Possibilistic planni...

This paper presents some prefiminary results of our current attempts to develop a hybrid multi-dimensional knowledge representation scheme which can handle both the incompleteness and uncertainty. We have started from the quantitative temporal constraint information, and extended it with possibilistic quantifiers. We have also extended the former towards a multidimensional constraint-based form...

The paper deals in a preliminary way with the problem of the selection of a unique fuzzy preference relation from the set of fuzzy preference relations. A direct algorithm of possibilistic clustering is the basis of the method of fuzzy preference relation discriminating. A method of decision-making based on a fuzzy preference relation is described and basic concepts of the heuristic method of p...

In search of semantic richer and more flexible database modelling and database querying techniques, different approaches based on fuzzy set theory have been developed. Among the most successful approaches are the possibilistic and similarity based models. More recently, extended possibilistic logic and various extensions to fuzzy sets have been applied to further enrich flexible database models...