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

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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Bibliographic Details
Main Authors: Soltani Moêz, Chaari Abdelkader, Ben Hmida Fayçal
Format: Article
Language:English
Published: Sciendo 2012-09-01
Series:International Journal of Applied Mathematics and Computer Science
Subjects:
takagi–sugeno fuzzy model
noise clustering algorithm
fuzzy c-regression model
orthogonal least squares
particle swarm optimization
Online Access:https://doi.org/10.2478/v10006-012-0047-0

Internet

https://doi.org/10.2478/v10006-012-0047-0

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