Convergence rates for a class of neural networks with logarithmic function
Feilong Cao, Yubo Yuan · 2009
The aim of this paper is to estimate the approximation error which results from the method of feedforward neural networks (FNNs) with logarithmic sigmoidal function s(x) = (1 + e-x)-1. By means of an extending function approach, a class of FNNs with single hidden layer and the active function s(x) is constructed to approximate the continuous function defined on a compact interval. By using the modulus of continuity of function as metric, the rate of convergence of the FNNs is estimated. Also, a numerical examples for illustrating the theoretical results is given.