Creator:

Soltani, Moez ; Chaari, Abdelkader ; Ben Hmida, Fayçal

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Title:

A novel Fuzzy C-Regression Model algorithm using a new error measure and particle swarm optimization

Group publication title:

AMCS, Volume 22 (2012)

Subject and Keywords:

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

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 algorith ; 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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2012

Resource Type:

artykuł

DOI:

10.2478/v10006-012-0047-0

Pages:

617-628

Source:

AMCS, Volume 22, Number 3 (2012) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego