The general type-2 FLS will have the ability to model uncertainty more accurately than interval type-2 sets, which, in turn, will result in the potential for a superior control performance in comparison to type-1 and interval type-2 FLSs. they are no longer a single number from 0 to 1, but are instead a continuous range of values between 0 and 1, say [a, b] (some people call this a blurring of the membership function value). one can either assign the same weighting or a variable weighting to the interval of membership function values [a, b]. When the latter is done, the resulting type-2 fuzzy set is called a general type-2 fuzzy set.
Published in Chapter:
Computational Intelligence Using Type-2 Fuzzy Logic Framework
A. Neogi (The University of Burdwan, India), A.C. Mondal (The University of Burdwan, India), and S.K. Mandal (National Institute of Technical Teachers’ Training & Research, India)
Copyright: © 2013
|Pages: 29
DOI: 10.4018/978-1-4666-2518-1.ch001
Abstract
In this chapter, the authors expand the notion of type-2 fuzzy sets. An introduction to standard and interval (type-2) fuzzy sets and systems is explained in the early part of the discussion. The chapter also covers the ideas of hybrid type-2 fuzzy system. Next, the authors study the applicability of type-2 fuzzy logic (FL) system in student’s performance in oral presentation as it is clearly new field of research topic and have an excellent opportunity to combine several fuzzy set method developed in the recent years. The proposed application shows the linkage of type-2 fuzzy system with TOPSIS. The present chapter also highlights the possible future directions for type-2 FL system research. By the end of the chapter, the authors hope that even those with little previous experience of fuzzy logic should be enabled to apply these methods in their own application areas and/or begin research in this fascinating and exciting area.