A defuzzification based new algorithm for the design of Mamdani-type fuzzy controllers

Saade, Jean Jamil. "A defuzzification based new algorithm for the design of Mamdani-type fuzzy controllers." Mathware and Soft Computing 7.2-3 (2000): 159-173. <http://eudml.org/doc/39196>.

@article{Saade2000,
abstract = {This paper presents a new learning algorithm for the design of Mamdani- type or fully-linguistic fuzzy controllers based on available input-output data. It relies on the use of a previously introduced parametrized defuzzification strategy. The learning scheme is supported by an investigated property of the defuzzification method. In addition, the algorithm is tested by considering a typical non-linear function that has been adopted in a number of published research articles. The test stresses on data-fitting, function shape representation, noise insensitivity and generalization capability. The results are compared with those obtained using neuro-fuzzy and other fuzzy system design approaches.},
author = {Saade, Jean Jamil},
journal = {Mathware and Soft Computing},
keywords = {Controladores difusos; Conjuntos difusos; Algoritmos de aprendizaje; learning algorithm; fuzzy controllers},
language = {eng},
number = {2-3},
pages = {159-173},
title = {A defuzzification based new algorithm for the design of Mamdani-type fuzzy controllers},
url = {http://eudml.org/doc/39196},
volume = {7},
year = {2000},
}

TY - JOUR
AU - Saade, Jean Jamil
TI - A defuzzification based new algorithm for the design of Mamdani-type fuzzy controllers
JO - Mathware and Soft Computing
PY - 2000
VL - 7
IS - 2-3
SP - 159
EP - 173
AB - This paper presents a new learning algorithm for the design of Mamdani- type or fully-linguistic fuzzy controllers based on available input-output data. It relies on the use of a previously introduced parametrized defuzzification strategy. The learning scheme is supported by an investigated property of the defuzzification method. In addition, the algorithm is tested by considering a typical non-linear function that has been adopted in a number of published research articles. The test stresses on data-fitting, function shape representation, noise insensitivity and generalization capability. The results are compared with those obtained using neuro-fuzzy and other fuzzy system design approaches.
LA - eng
KW - Controladores difusos; Conjuntos difusos; Algoritmos de aprendizaje; learning algorithm; fuzzy controllers
UR - http://eudml.org/doc/39196
ER -