A multilayer RBF network and its supervised learning

A general form of multilayer RBF networks is introduced. Complete supervised training rules for parameters are also presented. To achieve global convergence we apply a global optimization algorithm called the magic-brush method. This network can be naturally extended into a pyramid topology. Simulations show higher representation and generalization capability of the proposed networks comparing with the RBF and multilayer networks with sigmoid activation functions