A novel Fuzzy C-Regression Model algorithm using a new error measure and particle swarm optimization - Soltani, Moez; Chaari, Abdelkader; Ben Hmida, Fayçal (Zielona Góra: Uniwersytet Zielonogórski)
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
Keywords: Takagi-Sugeno fuzzy models; noise clustering algorithm; fuzzy c-regression model; orthogonal least squares; particle swarm optimization