Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions

By Jerry M. Mendel. 2001

2,725 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e867d7f519aa187c5c43cda9608e3da8f2a2f82d

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Citations in the corpus

A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots (2004, methodology): “The upper membership function is associated with the upper bound of and is denoted by , [17].”
Geometric Type-1 and Type-2 Fuzzy Logic Systems (2007, methodology): “However, for type-2 interval fuzzy set Karnik and Mendel [31], [28] developed the iterative method for type-reduction which is computationally efficient and is widely Fig.”
Solar Power Prediction Using Interval Type-2 TSK Modeling (2013, methodology): “Equations (7) and (8) are found using the bounds on the output of interval TSK systems [19].”
Computing with words and its relationships with fuzzistics (2007, methodology): “The Karnik–Mendel (KM) algorithms [8,15] are used to do this, and these algorithms are monotonically and super-exponentially convergent [21].”
A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations (2014, background): “Membership functions are subject to uncertainty arising from various sources [15, 17, 32].”
Computing derivatives in interval type-2 fuzzy logic systems (2004, methodology): “For detailed derivations of all of these results as well as more background on type-2 FLSs, see [6].”
A survey on industrial applications of fuzzy control (2011, unknown): “- the use of the additional parameterization offered by type-2 fuzzy logic in handling the uncertainties specific to industrial processes [238–244].”
A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets (2009, methodology): “[13] The centroid Cðe AÞ of an IT2 FS e A is the union of the centroids of all its embedded T1 FSs Ae, i.”
An efficient centroid type-reduction strategy for general type-2 fuzzy logic system (2008, methodology): “To date, type-reduction methods have been developed by Karnik and Mendel [3] and are elaborated upon in [1].”
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means (2007, methodology): “have suggested an iterative algorithm which estimates both ends of an interval, that is, estimating and without using all of the embedded fuzzy sets for an interval type-2 fuzzy set [1].”
Toward General Type-2 Fuzzy Logic Systems Based on zSlices (2010, background): “General type-2 FLSs have only recently been investigated in more detail [4]–[11], [16] as the high complexity associated with their design and their computational requirements made them appear unsuitable for real-world use.”
Advances in type-2 fuzzy sets and systems (2007, methodology): “For a relatively simple introduction to the former, see [54], and for the latter, see [47,55].”
Handbook of Granular Computing (2008, background): “, [56] as well as the corresponding chapters from this handbook).”
Type-2 FLCs: A New Generation of Fuzzy Controllers (2007, methodology): “…when compared to using type-1 fuzzy sets, as the uncertainty represented in the footprint of uncertainty in type-2 fuzzy sets lets us cover the same range as type-1 fuzzy sets with a smaller number of labels and the rule reduction will be greater when the number of the FLC inputs increases [25].”