[naeini1] in this paper, a new method is proposed to find the fuzzy optimal solution of fuzzy multi-objective linear programming problems (fmolpp) with fuzzy right hand side and fuzzy decision variables. due to the imprecise nature of available resources, determination of a definitive solution to the model seems impossible. therefore, the proposed model is designed in order to make fuzzy decisi...

Probabilistic clustering techniques use the concept of memberships to describe the degree by which a vector belongs to a cluster. The use of memberships provides probabilistic methods with more realistic clustering than “hard” techniques. However, fuzzy schemes (like the Fuzzy c Means algorithm, FCW are open sensitive to outliers. We review four existing algorithms, devised to reduce this sensi...

In this paper a general bottleneck combinatorial optimization problem with uncertain element weights modeled by fuzzy intervals is considered. A rigorous possibilistic formalization of the problem and solution concepts in this setting that lead to finding robust solutions under fuzzy weights are given. Some algorithms for finding a solution according to the introduced concepts and evaluating op...

In this paper a wide class of sequencing problems with imprecise parameters is discussed. The imprecision is modeled by using closed intervals and fuzzy intervals, whose membership functions are regarded as possibility distributions for the values of unknown parameters. A possibilistic interpretation of fuzzy problems is provided, some solution concepts are proposed and some algorithms for comp...

Fuzzy rule bases are built of linguistic, qualitative knowledge. By using fuzzy rules we are able to specify simple models of complex systems. But, we have to pay a price for this simpliication. In general, fuzzy knowledge is gradually incomplete and gradually inconsistent. This paper deals with the detection of such partial gaps of knowledge or local contradictions. In order to do so we introd...

A possibilistic framework for instance-based prediction is presented which formalizes the generalization beyond experience by means of fuzzy rules. In comparison with related instance-based approaches such as the well-known Nearest Neighbor classifier, this method distinguishes itself by the following: First, by suggesting (guaranteed) degrees of possibility for competing outcomes rather than m...

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 ...