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...

in this paper, we introduce and study the concept of $r$-fuzzy regular semi open (closed) sets in smooth topological spaces. by using $r$-fuzzy regular semi open (closed) sets, we define a new fuzzy closure operator namely $r$-fuzzy regular semi interior (closure) operator. also, we introduce fuzzy regular semi continuous and fuzzy regular semi irresolute mappings. moreover, we investigate the ...

Fuzzy techniques may be used to represent uncertainty in both data and knowledge, and fuzzy inference systems (FISs) may be used to reason with such uncertain data and knowledge. In this paper, definitions are first provided for conventional type-1 fuzzy sets, more complex type-2 fuzzy sets and the recently introduced non-stationary fuzzy sets. Two medical applications in which such fuzzy sets ...

The necessity of dealing with uncertainty in real wold problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions such as, type-2 fuzzy sets, interval valued fuzzy sets, Atanassov’s intuitionistic fuzzy sets, etc., have provided a wide range of tools able to deal with uncertainty in different type of problems...

Navigation of autonomous mobile robots in dynamic and unknown environments needs to take into account different kinds of uncertainties. Type-1 fuzzy logic research has been largely used in the control of mobile robots. However, type-1 fuzzy control presents limitations in handling those uncertainties as it uses precise fuzzy sets. Indeed type-1 fuzzy sets cannot deal with linguistic and numeric...

For generalised type-2 fuzzy sets the defuzzification process has historically been slow and inefficient. This has hampered the development of type-2 Fuzzy Inferencing Systems for real applications and therefore no advantage has been taken of the ability of type-2 fuzzy sets to model higher levels of uncertainty. The research reported here provides a novel approach for improving the speed of de...