A new ridge basis function neural network for data-driven modeling and prediction

This paper presents a new type of neural network called ridge basis function neural network (RiBFNN), which in structure comprises two submodels, namely a linear submodel and a ridge basis function submodel. The proposed model has the following advantages. It is known that linear models are transparent and easily interpretable, the inclusion of a linear submodel can therefore determine how the overall evolution trend of the system behavior (output or response) depends on the system input (or dependent) variables. On the other hand, the inclusion of the ridge basis functions enables the identification of nonlinearities that cannot be revealed by linear models and thus can improve the overall prediction performance. The proposed network is applied to a real data modeling task in relation to the relativistic electron flux intensity prediction in space weather study, and relevant performance of the network is presented.