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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...
Recently, type-2 fuzzy logic systems (T2 FLSs) have received increased research attention due to their potential to model and cope with the dynamic uncertainties ubiquitous in many engineering applications. However, because of the complex nature and the computational intensity of the inference process, only the constrained version of T2 FLSs, i.e., the interval T2 FLSs, was typically used. Fortunately,...
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...
Recently, Type-2 (T2) Fuzzy Logic Systems (FLSs) gained increased attention due to their capability to better describe, model and cope with the ubiquitous dynamic uncertainties in many engineering applications. By far the most widely used type of T2 FLSs are the Interval T2 (IT2) FLSs. This paper provides a comparative analysis of two fundamentally different approaches to defuzzification of IT2 Fuzzy...
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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