An approach to structure identification of fuzzy models

This paper deals with the structure identification problem for a fuzzy model, which is solved under the requirement of simplifying a fuzzy system once a satisfactory structure is available. Particularly, we propose a rule selection method to build a simplified version of the original rule base by preserving the model accuracy. The rule selection problem is formulated as a structure reduction process of the neuro-fuzzy network used to model a fuzzy system and is solved through an iterative algorithm aiming at selecting the minimal number of rules for the problem at hand. Experimental results demonstrate the algorithm's effectiveness in identifying reduced fuzzy models with equivalent performance to the original one