A radial basis function network is an artificial neural network that uses radial basis functions as activation functions. It is a linear combination of radial basis functions. They are used in function approximation, time series prediction, and control.

A radial basis function (RBF) is a real-valued function whose value depends only on the distance from the origin, so that φ(x)=φ(||x||); or alternatively on the distance from some other point c, called a center, so that φ(x,c)=φ(||x-c||). Any function φ that satisfies the property is a radial function. The norm is usually Euclidean distance, although other distance functions are also possible. For example by using probability metric it is for some radial functions possible to avoid problems with ill conditioning of the matrix solved to determine coefficients w_{i} (see below), since the ||x|| is always greater than zero.