An iterative fuzzy identification method hybridising modified objective cluster analysis with genetic algorithm
Na Wang, Yu Pu Yang · International Journal of Modelling Identification and Control · 2010
In this paper, an iterative fuzzy identification method hybridising modified objective cluster analysis with genetic algorithm (IFI-MOCA&GA) for Takagi-Sugeno-Kang (TSK) fuzzy modelling is proposed. By means of combining the presented modified objective cluster analysis with the fuzzy c-means (FCM) algorithm, the robust and compact fuzzy partition in the input space is obtained. Furthermore, this fuzzy partition is iteratively identified by means of hybridising the modified objective cluster analysis algorithm with the genetic algorithm. Therefore, the accuracy of the fuzzy partition is improved. Using this structure identification strategy, the trade-off between robustness, complexity and accuracy of the model is achieved. Finally, the consequent parameters are estimated by the stable Kalman filter (SKF) algorithm. The performance of the proposed method is shown to be superior to the other methods by a famous electrical simulation example.