in this paper, we present a revised similarity measure based onchen-and-chen's similarity measure for fuzzy risk analysis. the revisedsimilarity measure uses the corrected formulae to calculate the centre ofgravity points, therefore it is more  effective than the chen-and-chen'smethod. the revised similarity measure can overcome the drawbacks of theexisting methods. we have also proposed a new ...

Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such unc...

For general type-2 fuzzy sets, the defuzzification process is very complex, and the exhaustive direct method of implementing type-reduction is computationally expensive and turns out to be impractical. This has inevitably hindered the development of type-2 fuzzy inferencing systems in real world applications. The present situation will not be expected to change, unless an efficient and fast met...

This paper describes an interval type-2 fuzzy modeling framework, reduced-set vector-based interval type-2 fuzzy neural network (RV-based IT2FNN), to characterize the representation in fuzzy logic inference procedure. The model proposed introduces the concept of interval kernel to interval type-2 fuzzy membership, and provides an architecure to extract reduced-set vectors for generating interva...

Type-1 Fuzzy Logic Systems (FLSs) have been applied to date with great success to different applications. However, for many real-world applications, there is a need to cope with large amounts of uncertainties. The traditional Type-1 FLSs that use crisp Type-1 fuzzy sets cannot directly handle such uncertainties. Type-2 FLSs that use Type2 fuzzy sets can handle such uncertainties to produce a be...

The type-2 fuzzy sets was introduced by L. Zadeh as an extension of ordinary fuzzy sets. So the concept of type-2 fuzzy sets is also extended from type-1 fuzzy sets. If A is a type-1 fuzzy set and membership grade of x ∈ X in A is μA(x), which is a crisp number in [0, 1]. A type-2 fuzzy set in X is Ã, and the membership grade of x ∈ X in à is μÃ(x), which is a type-1 fuzzy set in [0, 1]. The el...

This paper proposes a fuzzy classifier based on type-2 fuzzy sets to be applied in land cover classification. The classifier is built on the basis of the available data and considers the merging of information drawn from different experts. The data regard a thematic mapper representing the land cover of a real plain cultivated area. The experts are represented by different bands which classify ...