System identification using neural networks
Hrushikesh N. Mhaskar, Nahmwoo Hahm · 2002
We examine the complexity of neural networks required to approximate an unknown system to a given degree of accuracy. We establish lower bounds on the number of neurons, as well as constructing networks to "almost" achieve this lower bound in the worst case analysis. Our constructions are simple, deterministic, and involve no optimization based training, such as backpropagation.