There exists a neural network that does not make avoidable mistakes

A. Ronald Gallant, Halbert White

Research output: Contribution to conferencePaperpeer-review

86 Scopus citations

Abstract

The authors show that a multiple-input, single-output, single-hidden-layer feedforward network with (known) hardwired connections from input to hidden layer, monotone squashing at the hidden layer and no squashing at the output embeds as a special case a so-called Fourier network, which yields a Fourier series approximation properties of Fourier series representations. In particular, approximation to any desired accuracy of any square integrable function can be achieved by such a network, using sufficiently many hidden units. In this sense, such networks do not make avoidable mistakes.

Original languageEnglish (US)
Pages657-664
Number of pages8
DOIs
StatePublished - 1988

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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