<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Research Unit on Control, Monitoring and Safety of Systems (C3S) High School of Sciences and Engineering of Tunis (ESSTT), 5, av. Taha Hussein, BP 56-1008 Tunis, Tunisia

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

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

Online veröffentlicht: 28. Sept. 2012

Seitenbereich: 617 - 628

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

Schlüsselwörter
Takagi–Sugeno fuzzy model, noise clustering algorithm, fuzzy c-regression model, orthogonal least squares, particle swarm optimization

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 28. Sept. 2012

Seitenbereich: 617 - 628

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

SchlüsselwörterTakagi–Sugeno fuzzy model, noise clustering algorithm, fuzzy c-regression model, orthogonal least squares, particle swarm optimization

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Takagi–Sugeno fuzzy model, noise clustering algorithm, fuzzy c-regression model, orthogonal least squares, particle swarm optimization