Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters

By Qilian Liang, Jerry M. Mendel. IEEE transactions on fuzzy systems, 2000

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Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching (2014, methodology): “Considering a nonlinear plant subject to parameter uncertainties represented by an IT2 T–S fuzzy model [31], [39], an IT2 fuzzy controller is proposed to perform the control process.”
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning (2008, background): “925907 of signal processing [4], [5], control [6]–[8], and medical appli-”
Type-2 FLCs: A New Generation of Fuzzy Controllers (2007, methodology): “Type-2 Fuzzy Logic Systems (FLSs) that use type-2 fuzzy sets have been used to date with great success where [14] used type-2 Fuzzy Adaptive Filters (FAF) in the equalization of nonlinear time-varying channels, where the type-2 based FAF has outperformed its type-1 counterpart.”
Stability Analysis of Interval Type-2 Fuzzy-Model-Based Control Systems (2008, methodology): “The applications include adaptive filtering [14], autonomous mobile robots [15], and dc–dc power converters [16].”
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means (2007, background): “For example, applications include the classification of coded video streams [5], co-channel interference elimination from nonlinear time-varying communication channels [6], connection admission control [7], control of mobile robots [8], [9], decision making [10], [11], equalization of nonlinear fadi”