Sampling rate for information encoding using multilayer neural networks

A new approach to band-limited function approximation using two-layer neural networks is presented. The Nyquist sampling rate theorem is used to solve for the optimum amount of learning data in n-dimensional input space. Choosing the least but still sufficient set of training vectors results in reduced number of hidden neurons and learning time for the network.