In this paper, application of possibilistic clustering techniques to identification of local linear models will be discussed. In particular, a generalisation of some possibilistic algorithms in the bibliography is obtained. With the presented procedures, a trade-off between an “expected shape” of the membership functions and model fit can be stated. Possibilistic clustering may allow for better...

Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain and/or inconsistent information exists. Based on our previous work, we develop a possibilistic description logic reasoner. Our demo will i...

The intended purpose of this research 1 is i) to show the specificity of possibilistic logic with respect to other logics with weighted statements, in particular its ability to deal both with uncertainty and vagueness, ii) to develop proof methods for possibilistic logic, and iii) to relate its ability to cope with partial inconsistency to belief revision and non-monotonic reasoning issues. See...

In this paper, an analysis of the convergence performance is conducted for a class of possibilistic clustering algorithms utilizing the Zangwill convergence theorem. It is shown that under certain conditions the iterative sequence generated by a possibilistic clustering algorithm converges, at least along a subsequence, to either a local minimizer or a saddle point of the objective function of ...

Possibilistic networks offer a qualitative approach for modeling epistemic uncertainty. Their practical implementation requires the specification of conditional possibility tables, as in the case of Bayesian networks for probabilities. This paper presents the possibilistic counterparts of the noisy probabilistic connectives (and, or, max, min, . . . ). Their interest is illustrated on an exampl...

This paper continues the authors’ research in stability analysis in possibilistic programming in that it extends the results in [7] to possibilistic linear programs with multiple objective functions. Namely, we show that multiobjective possibilistic linear programs with continuous fuzzy number coefficients are well-posed, i.e. small changes in the membership function of the coefficients may cau...

Probabilistic and possibilistic models of sequential decision problems are known to possess good behavioral algorithmic properties. In this paper, the range under uncertainty that dynamically consistent, consequentialist allow for tree reduction is enlarged by considering a representation both probabilistic possibilistic. The corresponding utility functional expected highly likely states, an op...

Possibilistic DL-Lite is an extension of the DL-Lite framework to deal with uncertain pieces of information. In this paper, we deal with inconsistency in DL-Lite in case where the assertions are uncertain and encoded in a possibility theory framework. We investigate three inconsistency-tolerant reasoning methods for possibilistic DL-Lite knowledge bases which are based on the selection of one c...

This paper deals with a portfolio selection problem with independently estimated possibilistic return rates. Under such a circumstance, a distributive investment has been regarded as a good solution in the traditional portfolio theory. However, the conventional possibilistic approach yields a concentrated investment solution. Considering the reason why a distributive investment is advocated, a ...