Robust possibilistic c-regression models algorithm
Moêz Soltani, Achraf Jabeur Telmoudi, Lotfi Chaouech, Abdelkader Châari · 2017
This paper studies the problem of the parameter identification based on fuzzy c-regression models for nonlinear systems. The novel procedure combines the possibilistic c-means procedure with fuzzy c-regression models (FCRM) in order to reduce the effects of noisy data. In comparison to the existing algorithms in the literature, the proposed method utilizes a generalized objective function that reduces the errors of partitioning data sets contaminated by noise and as a consequence an accurate model is obtained. The results of this study demonstrate the effectiveness of proposed method compared with other extended versions of FCRM algorithm.