In order to estimate fuzzy regression models, possibilistic and least-squares procedures can be considered. By taking into account a least-squares approach, regression models with crisp or fuzzy inputs and crisp or fuzzy output are suggested. In particular, for these fuzzy regression models, unconstrained and constrained (with inequality restrictions) least-squares estimation procedures are dev...

Conventional Fuzzy regression using possibilistic concepts allows the identification of models from uncertain data sets. However, some limitations still exist. This paper deals with a revisited approach for possibilistic fuzzy regression methods. Indeed, a new modified fuzzy linear model form is introduced where the identified model output can envelop all the observed data and ensure a total in...

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...

This paper presents a possibilistic (fuzzy) model in optimal siting and sizing of Distributed Generation (DG) for loss reduction and improve voltage profile in power distribution system. Multi-objective problem is developed in two phases. In the first one, the set of non-dominated planning solutions is obtained (with respect to the objective functions of fuzzy economic cost, and exposure) using...

-There are various clustering models introduced for unsupervised learning. PFCM or the possibilistic c-means model was proposed in 2005. PFCM produces mainly three values: the typicality values, membership values and the centres of the clusters. It is a hybrid model of PCM and FCM. We propose an extension to PFCM so that it can be used to cluster the text files. Keywords— possibilistic model, f...

In this paper we propose a propositional temporal language based on fuzzy temporal constraints which turns out to be expressive enough for domains –like many coming from medicine– where knowledge is of propositional nature and an explicit handling of time, imprecision and uncertainty are required. The language is provided with a natural possibilistic semantics to account for the uncertainty iss...

In this paper, fuzzy quantitative models of language statistics are constructed. All suggested models are based on the assumption about a superposition of two kinds of uncertainties: probabilistic and possibilistic. The realization of this superposition in statistical distributions is achieved by the probability measure splitting procedure. In this way, the fuzzy versions of generalized binomia...

Fuzzy inference systems are widely used for classification and control. They can be designed from the training data. This paper describes a technique for deriving fuzzy classification rules from the interval-valued data. The technique based on a heuristic method of possibilistic clustering and a special method of the interval-valued data preprocessing. Basic concepts of the heuristic method of ...

The present fuzzy arithmetic based on Zadeh’s possibilistic extension principle and on the classic definition of a fuzzy set has many essential drawbacks. Therefore its application to the solution of practical tasks is limited. In the paper a new definition of the fuzzy set is presented. The definition allows for a considerable fuzziness decrease in the number of arithmetic operations in compar...

This paper deals in a preliminary way with the problem of clustering of three-way data. A method of the problem solving is based on the application of a direct possibilistic clustering algorithm based on the concept of allotment among fuzzy cluster to a matrix of feeble fuzzy tolerance, which represent a structure of the set of objects under uncertainty. The paper provides the description of ba...