A multilayer RBF network and its supervised learning

In this paper a general form of multilayer RBF networks is introduced. A complete supervised training rules for parameters are also presented. To achieve global convergence we apply a global optimazation algorithm called magic-brush method. This network is then generalized to a pyramid network. Simulations shown higher representation and generalization capability of the proposed networks comparing with the RBF and multilayer networks with sigmoid activation functions.