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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