 We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
<meaning-changed>  We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%%  \times [0, 1] ^{n^+ \times } [0, 1] ^{n .
 We prove some general results which imply that an artificial neural network with a single hidden layer and sigmoid activation represents no function in L^{p(}%DIFDELCMD < \RR%%% ^2 \times 0, 1%DIFDELCMD < ]%%% ^{n neural network with a single hidden layer and logistic activation is a universal approximator of L^{2}( %DIFDELCMD < \RR %%% \times [0, 1] ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times } [0, 1] ^{n .