FUNCTIONAL EQUIVALENCE BETWEEN RADIAL BASIS FUNCTION NETWORKS AND FUZZY INFERENCE SYSTEMS

This letter shows that under some minor restrictions, the functional behavior of radial basis function networks (RBFN's) and fuzzy inference systems are actually equivalent. This functional equivalence enables us to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functional equivalent.