Original Contribution: Uniqueness of the weights for minimal feedforward nets with a given input-output map

  • Authors:
  • Héctor J. Sussmann

  • Affiliations:
  • -

  • Venue:
  • Neural Networks
  • Year:
  • 1992

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Abstract

Abstract: We show that, for feedforward nets with a single hidden layer, a single output node, and a ''transfer function'' Tanh s, the net is uniquely determined by its input-output map, up to an obvious finite group of symmetries (permutations of the hidden nodes, and changing the sign of all the weights associated to a particular hidden node), provided that the net is irreducible (i.e., that there does not exist an inner node that makes a zero contribution to the output, and there is no pair of hidden nodes that could be collapsed to a single node without altering the inputoutput map).