Type-2 Fuzzy Basis Functions for Adaptive Control

Kheireddine Chafaa(1*), Redha Benzid(2), Noureddine Slimane(3), Mouna Ghanai(4), Dolores Blanco(5), Luis Moreno(6)

(1) Université de Batna, Faculté de Technologie, Département d’Electronique, 05 avenue chahid Boukhlouf, 05000 Batna, Algeria
(2) Université de Batna, Faculté de Technologie, Département d’Electronique, 05 avenue chahid Boukhlouf, 05000 Batna, Algeria
(3) Université de Batna, Laboratoire d’Electronique Avancée, 05 avenue Chahid Boukhlouf, 05000 Batna, Algeria
(4) Université de Batna, Faculté de Technologie, Département d’Electronique, 05 avenue chahid Boukhlouf, 05000 Batna, Algeria
(5) Robotics Lab. Univ. Carlos III, Av. Universidad, 30 28911 Leganés (Madrid), Spain
(6) Robotics Lab. Univ. Carlos III, Av. Universidad, 30 28911 Leganés (Madrid), Spain
(*) Corresponding author


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Abstract


It has been proven that fuzzy systems called type-1 fuzzy systems can approximate any nonlinear function to any desired accuracy because of the universal approximation theorem. The principal problem encountered with type-1 fuzzy systems is that they can deliver a non satisfactory performance in face of uncertainty and imprecision. In this paper, a type-2 fuzzy membership functions were automatically determined in order to use them in fuzzy systems based on type-2 fuzzy basis functions.
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Keywords


Type-2 Fuzzy Logic; Clustering Algorithm; GKCA Algorithm; Fuzzy Basis Functions; Fuzzy Estimation; Adaptive Control

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