Fuzzy dynamic model identification by fuzzy c-regressoin models clustering
Chung‐Chun Kung, Yi-Fen Nieh, Jui-Yiao Su · 2010
This paper presents an algorithm to identify fuzzy dynamic (FD) model for a class of nonlinear plant. Firstly, the fuzzy c-regression models (FCRM) clustering technique is applied to partition the product space of the given input-output data into regression functional clusters. A novel cluster validity criterion with fuzzy hypervolume is set up to determine the appropriate number of clusters which has hyper-plane-shaped representatives. Furthermore, the fine-tuning procedures are included to adjust the antecedent and consequent parameters precisely. Finally, a FD model with compact number of IF-THEN rules could be generated systematically. A simulation example is provided to demonstrate the accuracy and effectiveness of the proposed algorithm.