Abstract
There has been a significant contribution to scientific literature in the design and applications of Type-2 fuzzy logic systems (T2FLS). The T2FLSs found applications in many aspects of our daily lives, such as engineering, pure science, medicine and social sciences. The online web of science was searched to identify the 100 most frequently cited papers published on the design and application of T2FLS from 1980 to 2016. The articles were analyzed based on authorship, source title, country of origin, institution, document type, web of science category, and year of publication. The correlation between the average citation per year (ACY) and the total citation (TC) was analyzed. It was found that there is a strong relationship between the ACY and TC (r = 0.91643, P<0.01), based on the papers consider in this research. The “Type -2 fuzzy sets made simple” authored by Mendel and John (2002), published in IEEE Transactions on Fuzzy Systems received the highest TC as well as the ACY. The future trend in this research domain was also analyzed. The present analysis may serve as a guide for selecting qualitative literature especially to the beginners in the field of T2FLS.
Highlights
The available data in most real world problems are quite associated with uncertainties in nature [1]
The results offer a well understanding of the trends in Type-2 fuzzy logic system (T2FLS)
The data on the design and applications of type-2 fuzzy logic systems reported in this study were derived from the
Summary
The available data in most real world problems are quite associated with uncertainties in nature [1]. These uncertaities are due to a deficiency in information that may be incomplete, imprecise, contradictory, unreliable, vague, fragmentary or deficient in some other ways. The primary feature of fuzzy reasoning allows for handling a different kind of uncertainties[3]. These include easy incorporation of expert knowledge into the control law, less model dependent, robust and can be used to model grammatical rules [4]. In T1FLS, the uncertainty is represented by a precise number in a range of (0, 1)
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