Adaptive fuzzy regression clustering algorithm for TSK fuzzy modeling

Abstract

The TSK type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Some approaches for modeling TSK fuzzy rules have been proposed in the literature. Most of them define their fuzzy subspaces bases based on the idea of training data being close enough instead of having similar functions. In addition, the fuzzy C-regression model (FCRM) clustering algorithm is proposed to construct TSK fuzzy models. However, this approach does not take into account the data distribution. In this paper, a novel TSK fuzzy modeling approach is presented. In this approach, adaptive fuzzy regression clustering (AFRC) algorithm is proposed to simultaneously define fuzzy subspaces and find the parameters in the consequent parts of TSK rules. In addition, the similarity measure is used to reduce the redundant rules in the clustering process. To obtain a more precise model, a gradient descent algorithm is employed. From the simulation results, the proposed TSK fuzzy model approach indeed showed superior performance.