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...

This paper presents the theory and design of interval type-2 fuzzy logic systems (FLSs) because of computational complexity of using general type-2 fuzzy set (T2FS) in type-2 fuzzy system. We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs; one that is based on a general inference formula for them. We introduce the concept of up...

In order to handle noisy measurement data, an interval type-2 fuzzy logic system (T2FLS) is proposed. In this paper, the evolution of soft fault diagnosis for analog circuits is explored based on a type-2 fuzzy logic system (T2FLS) which can handle measurement uncertainties. In order to evaluate the values of the faulty components accurately, a type-2 fuzzy rule base is adopted to describe the ...

The objective of this paper is to present an approach that utilizes an interval type-2 fuzzy decision making system (IT2FDMS) to quantify the Project Management Efficiency (PME). The algorithm developed in this paper is based upon interval type-2 fuzzy logic, giving it the ability to solve complex problems plagued with uncertainty and vagueness. A interval type-2 fuzzy decision making system is...

Conventional (type-1) fuzzy logic controllers have been commonly used in various power converter applications. Generally, in these controllers, the experience and knowledge of human experts are needed to decide parameters associated with the rule base and membership functions. The rule base and the membership function parameters may often mean different things to different experts. This may cau...

As Granular Computing has gained interest, more research has lead into using different representations for Information Granules, i.e., rough sets, intervals, quotient space, fuzzy sets; where each representation offers different approaches to information granulation. These different representations have given more flexibility to what information granulation can achieve. In this overview paper, ...