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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 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)
An integrated plastic microfluidic device was designed and fabricated for bacterial detection and identification. The device, made from poly(cyclic olefin) with integrated graphite ink electrodes and photopatterned gel domains, accomplishes DNA amplification, microfluidic valving, sample injection, on-column labeling, and separation. Polymerase chain(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 engine(More)
Type-2 fuzzy logic systems (FLSs) have been treated as a magic black box which can better handle uncertainties due to the footprint of uncertainty (FOU). Although the results in control applications are promising, the advantages of type-2 framework in fuzzy pattern classification is still unclear due to different forms of outputs produced by both systems.(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)