  Here we research the univariate quantitative approximation of real and
complex valued continuous functions on a compact interval or all the real line
by quasi-interpolation, Baskakov type and quadrature type neural network
operators. We perform also the related fractional approximation. These
approximations are derived by establishing Jackson type inequalities involving
the modulus of continuity of the engaged function or its high order derivative
or fractional derivatives. Our operators are defined by using a density
function induced by the error function. The approximations are pointwise and
with respect to the uniform norm. The related feed-forward neural networks are
with one hidden layer.
