RADIAL BASIS FUNCTION NETWORKS LEARNING TO SOLVE APPROXIMATION PROBLEMS

The purpose of the paper is the development and experimental study of new fast learning algorithms for radial basis function networks in solving approximation problems. To learn radial basis function networks, algorithms based on first-order methods have been developed for the first time: gradient descent with a pulse, Nesterov's accelerated gradient algorithm and RMSProp in combination with Nesterov's accelerated gradient. The advantages of sequential adjustment of parameters in each iterative cycle of network training are shown. The implementation of the Levenberg-Marquardt method for training radial basis function networks has been developed. With the help of the Levenberg-Marquardt method, the same results can be achieved as with the more complex algorithm of the method of trust regions. The developed algorithms have been experimentally studied.