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0899-7667
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Neural Computation

May 1993, Vol. 5, No. 3, Pages 359-362
(doi: 10.1162/neco.1993.5.3.359)
© 1993 Massachusetts Institute of Technology
Universal Approximation by Phase Series and Fixed-Weight Networks
Article PDF (152.42 KB)
Abstract

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.