On the relations between radial basis function networks and fuzzy systems

Numerical estimators of nonlinear functions can be constructed using systems based on fuzzy logic, artificial neural networks, and nonparametric regression methods. Some interesting similarities between fuzzy systems and some types of neural network models that use radial basis functions are discussed. Both these methods can be regarded as structural numerical estimators, because a rough interpretation can be given in terms of pointwise (local) rules. This explanation capability is important if the models are used as building blocks of expert systems. Most of the neural network models currently lack this capability, which the structural numerical estimators have intrinsically.<>