In Geoinformation systems (GIS) there is a need to model spatial regions with indeterminate boundary and under uncertainties. Although fuzzy logic methods are of great interest in many GIS applications, the traditional fuzzy logic has two important deficiencies. First, to apply the fuzzy logic, we need to assign, to every property and for every value, a crisp membership function. Second, fuzzy ...

In this paper, we investigate on-line fuzzy modeling for predicting the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (via fuzzy sets and fuzzy rule bases). The FLEXFIS and e...

Fuzzy systems, including fuzzy set theory and fuzzy logic, provide a rich and meaningful improvement, or extension of conventional logic. The mathematics generated by this theory is consistent, and fuzzy set theory may be seen as a generalisation of classic set theory. Applications in soil science, which may be generated from, or adapted to fuzzy set theory and fuzzy logic, are wide-ranging: nu...

The concept of the rough set was originally proposed by Pawlak as a formal tool for modelling and processing incomplete information in information systems, then in 1990, Dubois and Prade first introduced the rough fuzzy sets and fuzzy rough sets as a fuzzy extension of the rough sets. The aim of this paper is to present a new extension of the rough set theory by means of integrating the classic...

The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzz...

six-sigma has some measures which measure performance characteristics related to a process. in most of the traditional methods, exact estimation is used to assess these measures and to utilize them in practice. in this paper, to estimate some of these measures, including defects per million opportunities (dpmo), defects per opportunity (dpo), defects per unit (dpu) and yield, a new algorithm ba...

in this work, we introduce notion of connectedness on fuzzy soft topological spaces and present fundamentals properties. we also investigate effect to fuzzy soft connectedness. moreover, c_i-connectedness which plays an important role in fuzzy topological space extend to fuzzy soft topological spaces.

In this note, we introduce and study a concept of fuzzy equalization. Fuzzy equalization concerns a process of building information granules that are semantically and experimentally meaningful. The experimental relevance of a given information granule (fuzzy set) is directly linked with an encapsulation of a certain experimental evidence conveyed by the respective probability density function o...

In many applications, multiple correlation and partial correlation for three or more fuzzy sets are very important, but Chiang and Lin (1999, Fuzzy Sets and Systems 102: 221–226) do not solve this problem. Here, we propose a method to calculate the multiple correlation and partial correlation for fuzzy data, by adopting the concepts from the multivariate correlation model. In order to fit into ...