Improving algorithms for learning of radial basis functions networks for approximation problems and solving partial differential equations

Abstract

The learning of radial basis functions networks for solving approximation problems and partial differential equations is considered. Realizations of the accelerated gradient of Nesterov and Le-venberg-Marquardt were proposed for learning networks, which made it possible to significantly reduce the training time.