Interval type-2 fuzzy logic systems

By Qilian Liang, Jerry M. Mendel. Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2000

1,761 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b99bc9f3445b34b3e2a1718c7b48aa9f9e566186

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Citations in the corpus

Computing derivatives in interval type-2 fuzzy logic systems (2004, background): “13 When the input, , is located in a specific -domain segment, we call its corresponding MF branch an active branch ([4], [6]), e.”
Interval Type-2 Fuzzy Logic Systems Made Simple (2006, background): “To-date, because of the computational complexity of using a general T2 FS, most people only use interval T2 FSs in a T2 FLS, the result being an interval T2 FLS (IT2 FLS) [9].”
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots (2004, methodology): “We defuzzify the interval set by using the average of and , hence, the defuzzified crisp output for each output is [14], [17] Single rule base FLC (type-1 or type-2) suffer from the serious limitation that the number of rules increases exponentially with the number of variables involved [23].”
Optimization of Interval Type-2 Fuzzy Logic Controllers for a Perturbed Autonomous Wheeled Mobile Robot Using Genetic Algorithms (2009, background): “Hence, a type-2 membership grade can be any subset in [0, 1], the primary membership, and corresponding to each primary membership, there is a secondary membership (which can also be in [0, 1]) that defines the possibilities for the primary membership [27].”
Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters (2000, methodology): “A theory and design methodology for interval type-2 Mamdani FLSs is given in [17] and a comparable theory and design methodology for normalized output interval type-2 TSK FLSs is given in [18].”
Operations on type-2 fuzzy sets (2001, methodology): “The meet of interval sets under product = minimum t-norm has been given by Schwartz [26] and Kaufmann and Gupta [14], and is discussed in detail in Liang and Mendel [17].”
Advances in type-2 fuzzy sets and systems (2007, background): “More specifically, for an interval singleton T2 FLS [34]: (a) the firing set becomes a firing interval, i.”
Interval Neutrosophic Sets (2004, neutral): “The interval neutrosophic set is a generalization of classic set, fuzzy set, interval valued fuzzy set, intuitionistic fuzzy sets, interval valued intuitionistic fuzzy set, interval type-2 fuzzy set [3] and paraconsistent set.”
Fuzzy human motion analysis: A review (2014, methodology): “Taking this into account, [80] employed the interval type-2 FIS [81] which is capable of handling higher uncertainty levels present in the real world dynamic environments.”
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning (2008, methodology): “3) Layer 3 (Firing Layer): Each node in this layer is a rule node, and performs the fuzzy meet operation [24] using an algebraic product operation.”