Affine Takagi-Sugeno fuzzy model identification based on a novel fuzzy c-regression model clustering and particle swarm optimization
Moêz Soltani, Talel Bessaoudi, Abdelkader Châari, Faycal BenHmida · 2012
In this paper, a novel Takagi-Sugeno fuzzy model identification based on a new fuzzy c-regression model clustering algorithm and particle swarm optimization is presented. The main motivation for this work is to develop an identification procedure for nonlinear systems taking into account the noise. In addition, a new distance is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Thereafter, particle swarm optimization is employed to fine tune parameters of the obtained fuzzy model. The performance of the proposed approach is validated by studying the nonlinear plant modeling problem.