A hybrid model composed of a multilayer perceptron and a radial basis function network

Feedforward neural networks are divided into two classes according to characteristics of hidden units: one based on units with non-local activation functions, such as multilayer perceptrons (MLPs), the other based on units with local activation functions, such as radial basis function networks (RBFNs). Though both MLPs and RBFNs theoretically have ability to represent arbitrary functions, they cannot practically acquire sufficient approximation accuracy in most cases. In order to obtain more precise approximation, a hybrid model composed of an MLP and an RBFN is proposed. The performance of a single MLP, a single RBFN, and the proposed model is compared by function approximation problems. The results show that the proposed model is superior to the others in most cases, with regard to not only approximation accuracy but also learning speed.