Bounds on rates of variable-basis and neural-network approximation

The tightness of bounds on rates of approximation by feedforward neural networks is investigated in a more general context of nonlinear approximation by variable-basis functions. Tight bounds on the worst case error in approximation by linear combinations of n elements of an orthonormal variable basis are derived.