Universal Approximation by Phase Series and Fixed-Weight Networks
Neil E. Cotter, Peter R. Conwell · Neural Computation · 1993
In this note we show that weak (specified energy bound) universal approximation by neural networks is possible if variable synaptic weights are brought in as network inputs rather than being embedded in a network. We illustrate this idea with a Fourier series network that we transform into what we call a phase series network. The transformation only increases the number of neurons by a factor of two.