Interactive gradient algorithm for radial basis function networks

In this paper the radial basis function neural network is divided into two parts: (1) the input and the hidden layer, (2) the output layer, and the parameters of the two parts are trained through an interactive gradient learning algorithm. Experimental results in function approximation are more attractive, which show that the algorithm not only avoids the slow rate of the conventional gradient algorithm, but also reduces the nonlinear degree of the radial basis function neural network.