A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization

Moêz Soltani; Abdelkader Chaari; Fayçal Ben Hmida

International Journal of Applied Mathematics and Computer Science (2012)

  • Volume: 22, Issue: 3, page 617-628
  • ISSN: 1641-876X

Abstract

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This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.

How to cite

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Moêz Soltani, Abdelkader Chaari, and Fayçal Ben Hmida. "A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization." International Journal of Applied Mathematics and Computer Science 22.3 (2012): 617-628. <http://eudml.org/doc/244058>.

@article{MoêzSoltani2012,
abstract = {This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.},
author = {Moêz Soltani, Abdelkader Chaari, Fayçal Ben Hmida},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {Takagi-Sugeno fuzzy model; noise clustering algorithm; fuzzy c-regression model; orthogonal least squares; particle swarm optimization},
language = {eng},
number = {3},
pages = {617-628},
title = {A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization},
url = {http://eudml.org/doc/244058},
volume = {22},
year = {2012},
}

TY - JOUR
AU - Moêz Soltani
AU - Abdelkader Chaari
AU - Fayçal Ben Hmida
TI - A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
JO - International Journal of Applied Mathematics and Computer Science
PY - 2012
VL - 22
IS - 3
SP - 617
EP - 628
AB - This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
LA - eng
KW - Takagi-Sugeno fuzzy model; noise clustering algorithm; fuzzy c-regression model; orthogonal least squares; particle swarm optimization
UR - http://eudml.org/doc/244058
ER -

References

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