A note on error bounds for function approximation using nonlinear networks

For many problems in classification, compensation, adaptivity, identification, and signal processing, results concerning the representation and approximation of nonlinear functions can be of particular interest to engineers. Here we consider a large class of functionsf that map ⁿ into the set of real or complex numbers, and we give bounds on the number of parameters of a certain approximation network so thatf can be approximated to within a prescribed degree of accuracy using an appropriate configuration of the network. We also describe related work in the neural networks literature.