The rate of approximation of Gaussian radial basis neural networks in continuous function space

There have been many studies on the dense theorem of approximation by radial basis feedforword neural networks, and some approximation problems by Gaussian radial basis feedforward neural networks (GRBFNs) in some special function space have also been investigated. This paper considers the approximation by the GRBFNs in continuous function space. It is proved that the rate of approximation by GRNFNs with n (d) neurons to any continuous function f defined on a compact subset K aS, a"e (d) can be controlled by omega(f,n (-1/2)), where omega(f, t) is the modulus of continuity of the function f.