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General Type-2 Fuzzy Logic Systems (GT2 FLSs) are an extension to Type-1 (T1) FLS where at least one Fuzzy Set (FS) is a GT2 FS. However, due to the high computational complexity of operations on GT2 FSs, GT2 FLSs have been rarely used in practical applications. Instead, Interval Type-2 (IT2) FLSs which employ constrained IT2 FSs, have been widely used. Despite their superior computational complexity,...
General Type-2 Fuzzy Sets (GT2 FSs) have been originally proposed to allow for modeling uncertainty associated with the membership grades of Type-1 (T1) FSs. However, because of the computational complexity associated with the processing of GT2 FSs, only their constrained version, the Interval T2 (IT2) FSs, have been widely used. While IT2 FSs allow for fast processing, they lack the expressive power...
Type-2 Fuzzy Logic Systems (T2 FLSs) have been commonly attributed with the capability to model various sources of data uncertainties. The input uncertainties of an FLS were modeled using T2 Fuzzy Sets (FSs) and the type-reduced centroid of the output FS was interpreted as a measure of uncertainty associated with the terminal real-valued output. However, the accuracy of this input-output uncertainty...
Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have been commonly attributed with the capability to model and cope with dynamic uncertainties. However, the interpretation of this uncertainty modeling using the IT2 FLSs have been rarely addressed or taken into consideration during the design of the respective fuzzy controller. This paper extends the previously proposed method for incorporating the...
In the past decade Type-2 Fuzzy Logic Systems (T2 FLSs) gained increased research attention due to their potential to outperform Type-1 FLSs in applications with dynamic uncertainties. This advantage is typically attributed to the capability of T2 Fuzzy Sets (FSs) to better model the dynamic uncertainty and cope with its negative impacts. However, the accuracy, correctness and interpretation of such...
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