A new algorithm for structure optimization in fuzzy neural networks

AbstractReduced complexity in a fuzzy neural network eases the computational burden of construction and training from data, while enhancing the interpretability of the final model. Such structure optimisation can be done either by adjusting the number of inputs and the size of the rule set. In the literature these have generally been addressed independently (Sugeno and Yasukawa [1993], Hong and Harris [2003]). This paper presents a new algorithm where both structural parameters for a fuzzy neural network model are optimized together. Results from simulation examples are given to illustrate the new approach and confirm its advantage over existing methods.