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

• Autorzy: Moêz Soltani , Abdelkader Chaari , Fayçal Ben Hmida
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2012
• Tom: 22
• Numer: 3
• Strony: 617-628

Daty

wydano

2012

otrzymano

2011-08-05

poprawiono

2012-01-09

poprawiono

2012-04-21

Twórcy

autor

Moêz Soltani

• 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

autor

Abdelkader Chaari

• 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

autor

Fayçal Ben Hmida

• 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

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Identyfikatory

DOI

10.2478/v10006-012-0047-0