The book comprises 14 chapters and three appendices. The chapters are organized onto four parts: Preliminaries, Type-1 Fuzzy Logic Systems, Type-2 Fuzzy sets, and Type-2 Fuzzy Logic Systems. The book proves to be a valuable resource for professionals seeking to work with fuzzy sets in general and type-2 fuzzy sets in particular.
— This paper focuses on evolving type-2 fuzzy logic controllers (FLCs) genetically and examining whether they are better able to handle modelling uncertainties. The study is conducted by utilizing a type-2 FLC, evolved by a genetic algorithm (GA), to control a liquid-level process. A two stage strategy is employed to design the type-2 FLC. First, the… (More)
— A type-2 fuzzy set is characterized by a concept called footprint of uncertainty (FOU). It provides the extra mathematical dimension that equips type-2 fuzzy logic systems (FLSs) with the potential to outperform their type-1 counterparts. While a type-2 FLS has the capability to model more complex relationships, the output of a type-2 fuzzy inference… (More)
This paper introduces an alternative type-reduction method for interval type-2 (IT2) fuzzy logic systems (FLSs), with either continuous or discrete secondary membership function. Unlike the Karnik-Mendel type reducer which is based on the wavy-slice representation of a type-2 fuzzy set, the proposed type reduction algorithm is developed using the… (More)
Type-2 fuzzy sets, which are characterized by membership functions (MFs) that are themselves fuzzy, have been attracting interest. This paper focuses on advancing the understanding of interval type-2 fuzzy logic controllers (FLCs). First, a type-2 FLC is evolved using Genetic Algorithms (GAs). The type-2 FLC is then compared with another three GA evolved… (More)
— There has been an increasing amount of research on type-2 fuzzy logic systems (FLSs) recently. The interest is fueled by results demonstrating that type-2 fuzzy sets offer a framework for effectively solving problems where uncertainties are present. A concept, known as the footprint of uncertainty (FOU), is mainly responsible for the improved modeling… (More)
Many experiments have shown that interval type-2 (IT2) fuzzy PI controllers are generally more robust than their type-1 (T1) counterparts, as they are better able to cope with disturbances and uncertainties and eliminate oscillations. This paper aims at providing theoretical explanations to these experimental observations. Analysis has shown that the… (More)