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2012 | 22 | 3 | 617-628
Tytuł artykułu

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

Treść / Zawartość
Warianty tytułu
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.
Rocznik
Tom
22
Numer
3
Strony
617-628
Opis fizyczny
Daty
wydano
2012
otrzymano
2011-08-05
poprawiono
2012-01-09
poprawiono
2012-04-21
Twórcy
  • 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
  • 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
  • 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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Typ dokumentu
Bibliografia
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