We have already formalized several models of the possibilistic linear regression analysis, where it is assumed that possibilistic parameters are non-interactive, i.e., the joint possibilistic distribution of parameters is defined by minimum operators. In this paper, we will deal with the interactive case in which quadratic membership functions defined by A. Celmins are considered. With the same...

This paper considers a portfolio selection problem with type-2 fuzzy future returns involving ambiguous and subjectivity. Since this proposed problem is not well-defined due to fuzziness, introducing the fuzzy goal for the total future return and the degree of possibility, the main problem is transformed into the standard fuzzy programming problem including the secondary fuzzy numbers. Furtherm...

An architecture for the implementation of possibilistic models in an object-oriented programming environment (C++ in particular) is described. Fundamental classes for special and general random sets, their associated fuzzy measures, special and general distributions and fuzzy sets, and possibilistic processes are speciied. Supplementary methods|including the fast MM obius transform, the maximum...

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...

This paper discusses a multi-objective portfolio optimization problem for practical portfolio selection in fuzzy environment, in which the return rates and the turnover rates are characterized by fuzzy variables. Based on the possibility theory, fuzzy return and liquidity are quantified by possibilistic mean, and market risk and liquidity risk are measured by lower possibilistic semivariance. T...

Possibilistic Defeasible Logic Programming (\Pdelp) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at object-language level. The aim of this paper is twofold: first to present an approach towards extending \Pdelp in order to incorporate fuzzy constants and fuzzy unification, and af...

Defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning from incomplete and potentially inconsistent knowledge. Defeasible Logic Programming (DeLP) is a defeasible argumentation formalism based on an extension of logic programming. Although DeLP has been successfully integrated in a number of different real-world applications, D...

Compared with the conventional probabilistic mean-variance methodology, fuzzy number can better describe an uncertain environment with vagueness and ambiguity. Based on this fact, possibilistic mean-variance utilities to portfolio selection for bounded assets are discussed in this paper. The possibilistic mean value of the expected return is termed measure of investment return and the possibili...

This paper presents a latest survey of different technologies using fuzzy clustering algorithms. Clustering approach is widely used in biomedical field like image segmentation. A different methods are used for medical image segmentation like Improved Fuzzy C Means(IFCM), Possibilistic C Means(PCM),Fuzzy Possibilistic C Means(FPCM), Modified Fuzzy Possibilistic C Means(MFPCM) and Possibilistic F...

Optimization is a procedure of finding and comparing feasible solutions until no better solution can be found. It can be divided into several fields, one of which is the Convex Optimization. It is characterized by a convex objective function and convex constraint functions over a convex set which is the set of the decision variables. This can be viewed, on the one hand, as a particular case of ...