This work deals with the problem of identification of Discrete Possibilistic Dynamic System (DPDS), using the technologies of Genetic Algorithmms (GA). Applying the results from [5-9,11-13,15,16,18-20], the fuzzy recurrent process with possibilistic uncertainty, the source of which is expert knowledge reflections on the states of evolutionary complex extremal system, is constructed. The dynamic...

In this paper, we examine the performance of fuzzy clustering algorithms as the major technique in pattern recognition. Both possibilistic and probabilistic approaches are explored. While the Possibilistic C-Means (PCM) has been shown to be advantageous over Fuzzy C-Means (FCM) in noisy environments, it has been reported that the PCM has an undesirable tendency to produce coincident clusters. R...

In any learning process, the learners arrive with a great deal of variables, such as their different learning styles, their affective states and their previous knowledge, among many others. In most cases, their previous knowledge is incomplete or it comes with a certain degree of uncertainty. Possibilistic Logic was developed as an approach to automated reasoning from uncertain or prioritized i...

Within the possibilistic approach to uncer­ tainty modeling, the paper presents a modal logical system to reason about qualitative (comparative) statements of the possibility (and necessity) of fuzzy propositions. We re­ late this qualitative modal logic to the many­ valued analogues MVS5 and MVKD45 of the well known modal logics of knowledge and be­ lief 55 and KD45 respectively. Completeness ...

We explore an approach to possibilistic fuzzy c-means clustering that avoids a severe drawback of the conventional approach, namely that the objective function is truly minimized only if all cluster centers are identical. Our approach is based on the idea that this undesired property can be avoided if we introduce a mutual repulsion of the clusters, so that they are forced away from each other....

The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, this 'method' is extended to the well-known fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. It is realized by substitution of a kernel-induced distance metric for the original Euclidean distance, and the corresponding algori...

Several supply chain and production planning models in the literature assume the demands are fuzzy but most of them do not offer a specific technique to derive the fuzzy demands. In this study, we propose a methodology to obtain a fuzzy-demand forecast that is represented by a possibilistic distribution. The fuzzy-demand forecast is found by aggregating forecasts based on different sources; nam...

Recently, a good set of logic programming semantics has been defined for capturing possibilistic logic program. Practically all of them follow a credulous reasoning approach. This means that given a possibilistic logic program one can infer a set of possibilistic models. However, sometimes it is desirable to associate just one possibilistic model to a given possibilistic logic program. One of t...

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP p...

In refinery, fuel gas which is continuously generated during the production process is one of the most important energy sources. Optimal scheduling of fuel gas system helps the refinery to achieve energy cost reduction and cleaner production. However, imprecise natures in the system, such as prediction of production rate of fuel gas, prediction of energy demand of the equipments and cost coeffi...