Risk aversion is one of the main themes in risk theory. Risk theory is treated usually by probability theory. The aim of this paper is to propose an approach of the risk aversion by possibility theory initiated by Zadeh in 1978 as an alternative of probability theory in the modeling of uncertain situations. The main notions studied in this paper are the possibilistic risk premium and the possib...

In this paper, we investigate how linguistic information can be incorporated into classical propositional logic. First, we show that Zadeh’s extension principle can be justified and at the same time generalized by considerations about transformation of possibility measures. Using these results, we show how linguistic uncertainty about the truth value of a proposition leads to the introduction o...

We prove that possibilistic linear programming problems (introduced by Buckley in [2]) are well-posed, i.e. small changes of the membership function of the parameters may cause only a small deviation in the possibilistic distribution of the objective function.

Based on our earlier work on partial logics and extended logic programs [Wag91, Wag94, HJW96], and on the possibilistic logic of [DLP94], we de ne a compositional possibilistic rst-order logic with two kinds of negation.

Multi-objective De Novo linear programming (MODNLP) is problem for designing optimal system by reshaping the feasible set (Fiala [3] ). This paper deals with MODNLP having possibilistic objective functions coefficients. The problem is considered by inserting possibilistic data in the objective functions coefficients. The solution of the problem is defined and established under the using of effi...

Approximate reasoning with words is one of the remarkable human capability that manipulates perceptions in a wide variety of physical and mental tasks whether in fuzzy or uncertain surroundings. To model this remarkable human capability, L.A. Zadeh (1999) proposed a new concept of "computing with words", which is a methodology in which the objects of computation are words and propositions drawn...

An overview of fuzzy c-means clustering algorithms is given where we focus on different objective functions: they use regularized dissimilarity, entropy-based function, and function for possibilistic clustering. Classification functions for the objective functions and their properties are studied. Fuzzy c-means algorithms using kernel functions is also discussed with kernelized cluster validity...

Variable consistency and variable precision models for dominance-based fuzzy rough set analysis of possibilistic information systems Tuan-Fang Fan a , Churn-Jung Liau b & Duen-Ren Liu c a Department of Computer Science and Information Engineering , National Penghu University of Science and Technology , Penghu , Taiwan b Institute of Information Science , Academia Sinica , Taipei , Taiwan c Inst...

Construction of Fuzzy Control Charts Based on Weighted Possibilistic Mean Dabuxilatu Wang, Pinghui Li & Masami Yasuda a School of Economics and Statistics, Guangzhou University, Guangzhou, China b Research Center of Statistical Science Lingnan, Guangzhou University, Guangzhou, China c Faculty of Science, Chiba University, Chiba, Japan Accepted author version posted online: 16 Apr 2014.Published...

Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...