An approach to online identification of Takagi-Sugeno fuzzy models

By Plamen Parvanov Angelov, Dimitar Filev. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2004

1,006 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/18b4db8a705a65c277912fc13647b844c1de54d0

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Citations in the corpus

Signal and Data Processing for Machine Olfaction and Chemical Sensing: A Review (2012, methodology): “For details of the technique the reader is referred to [176].”
A review of novelty detection (2014, methodology): “A recursive expression derived in [85,146] is then used to update this estimation online.”
Fuzzy human motion analysis: A review (2014, methodology): “To this extent, [121] proposed the evolving Takagi–Sugeno fuzzy model [122,123] which can be seen as the fuzzy weighted mixture of the Kalman filter for object tracking in the video streams, and the performance is better than the ordinary Kalman filter.”
Interpretable Approximation of a Deep Reinforcement Learning Agent as a Set of If-Then Rules (2019, methodology): “• Fuzzy rules are extracted automatically from the newly created data set using Evolving Takagi-Sugeno (ETS) method [5].”
Clustering: A neural network approach (2010, neutral): “In Angelov and Filev (2004), an on-line clustering method has been implemented based on a first-order Cauchy type potential function.”
A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning (2008, methodology): “Apart from the aforementioned methods, most automatic fuzzy system optimization methods used neural learning algorithms [14]–[19].”
SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network (2009, methodology): “In this study, a modified least-square algorithm is used [2], [3], [24], [27], [29], [31], [33].”
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models (2008, methodology): “Its simplified version, Simp_eTS [10], extends eTS [9] by including an online rule base simplification algorithm based on the population of the rules, determined as the number of data samples belonging to the corresponding clusters.”
Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction (2006, methodology): “For the first system identification problem, performance of SAFIS is compared with other well known sequential algorithms such as MRAN [20], RANEKF [11], eTS [1], Simpl_eTS [2] and HybridAlgorithm (HA) [19].”
Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems (2009, methodology): “They include dynamic evolving neurofuzzy inference system (DENFIS) [4], evolving Takagi–Sugeno (eTS) model [5], Simplified eTS (Simpl_eTS) algorithms [6], and sequential adaptive fuzzy inference system (SAFIS) [7].”
Design of sliding mode unknown input observer for uncertain Takagi-Sugeno model (2007, neutral): “Matrices Ai, Bi, Ri and Ci can be obtained by using the direct linearization of an a priori nonlinear model around operating points, or alternatively by using an identification procedure [30], [31], [32].”