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

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...

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Veröffentlicht in:International Journal of Applied Mathematics and Computer Science Jg. 22; H. 3; S. 617 - 628
Hauptverfasser: Soltani, Moêz, Chaari, Abdelkader, Ben Hmida, Fayçal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Zielona Góra Versita 01.09.2012
Sciendo
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
Algorithms
Error analysis
Fuzzy
fuzzy c-regression model
Fuzzy logic
Fuzzy set theory
Mathematical analysis
Mathematical models
noise clustering algorithm
Optimization
orthogonal least squares
particle swarm optimization
Takagi–Sugeno fuzzy model
ISSN:1641-876X, 2083-8492, 2083-8492
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:v10006-012-0047-0.pdf
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ISSN:1641-876X
2083-8492
2083-8492
DOI:10.2478/v10006-012-0047-0