Study of approximation performance of simple function based on Gaussian -RBF neural networks
Ding Shuo
Chang Xiao-heng
Wu Qing-hui
Abstract：In order to study Gaussian -RBF neural networks'approximation ability of single -variable non-linearity function , Gaussian-RBF neural networks and BP neural networks are designed .And three typical sin-gle-variable nonlinearity functions , namely sine function , exponential function and step function are taken as examples to be approximated via two kinds of neutral networks .Simulation results show that for single -variable nonlinearity functions , Gaussian-RBF neural networks are superior to BP neural networks in approximation pre-cision, convergence rate as well as approximation performance .Thus they provide an ideal method for the solu-tion of single-variable nonlinearity function approximation .
Keywords：Gaussian fun ctionRBF neural networksBP reural networksFunction approximationSimnlation
Publication Date：2013-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 300-304 )