On the design of a class of neural networks

Abstract The design of the class of RBF networks is described. These three-layer networks possess the universal and best approximation capability in the framework of supervised learning of real functions of real vectors from examples and offer a large amount of applications. A learning method is described for finite network and data size, which is quite efficient for noise-free and noisy data. Its robust and efficient implementation as well as its evaluation is described.