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-2 fuzzy sets (T2 FSs), originally introduced by Zadeh [3], provide additional design degrees of freedom in Mamdani and TSK fuzzy logic systems (FLSs), which can be very useful when such systems are used in situations where lots of uncertainties are present [4]. The implementation of this type-2 FLS involves the operations of fuzzification, inference,and output processing. We focus on ―outp...

Type-2 systems has been becoming the focus of research in the field of fuzzy logic in recent years. Comparing with type-1 systems, type-2 fuzzy systems are more complex and relatively more difficult to understand and implement. We developed an interactive graphical user interface (GUI) based toolbox, MFLS tool, for interval type-2 fuzzy logic system. This paper presents MFLS toolbox. Moreover, ...

We describe a method for edge detection in digital images based on the morphological gradient and fuzzy logic. The goal is to improve one of the basic methods for edge detection in order to obtain a better result even without applying any filter to the image. The tests were made with a type-1 fuzzy inference system (T1FIS) and with an interval type-2 fuzzy inference system (IT2FIS). We show tha...

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 ...