  In this short note, we give an elementary proof of a universal approximation
theorem for neural networks with three hidden layers and increasing,
continuous, bounded activation function. The result is weaker than the best
known results, but the proof is elementary in the sense that no machinery
beyond undergraduate analysis is used.
