A neuro-fuzzy model reduction strategy

This paper presents an approach to obtain simple fuzzy models. The simplification strategy involves structure reduction of a neural network modeling the fuzzy system and is carried out through an iterative algorithm aiming at selecting a minimal number of rules for the problem at hand. The selection algorithm allows manipulation of the neuro-fuzzy model to minimize its complexity and to preserve a good level of accuracy. Experimental results demonstrate the algorithm's effectiveness in identifying reduced neuro-fuzzy networks with no degradation in the original performance