On analog implementations of discrete neural networks

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The paper will show that in order to obtain minimum size neural networks (i.e., size-optimal) for implementing any Boolean function, the nonlinear activation function of the neutrons has to be the identity function. The authors shall shortly present many results dealing with the approximation capabilities of neural networks, and detail several bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions they will show that implementing Boolean functions can be done using neurons having an identity nonlinear function. It follows that size-optimal solutions can be obtained only using analog circuitry. Conclusions, and several comments ... continued below

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8 p.

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Beiu, V. & Moore, K.R. December 1, 1998.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

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  • Los Alamos National Laboratory
    Publisher Info: Los Alamos National Lab., Div. of Space and Atmospheric Sciences, NM (United States)
    Place of Publication: New Mexico

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Description

The paper will show that in order to obtain minimum size neural networks (i.e., size-optimal) for implementing any Boolean function, the nonlinear activation function of the neutrons has to be the identity function. The authors shall shortly present many results dealing with the approximation capabilities of neural networks, and detail several bounds on the size of threshold gate circuits. Based on a constructive solution for Kolmogorov`s superpositions they will show that implementing Boolean functions can be done using neurons having an identity nonlinear function. It follows that size-optimal solutions can be obtained only using analog circuitry. Conclusions, and several comments on the required precision are ending the paper.

Physical Description

8 p.

Notes

OSTI as DE99000763

Source

  • FLINS workshop: fuzzy logic and intelligent technologies for nuclear science and industry, Antwerp (Belgium), 14-16 Sep 1998

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  • Other: DE99000763
  • Report No.: LA-UR--98-1609
  • Report No.: CONF-980942--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 677167
  • Archival Resource Key: ark:/67531/metadc707529

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Office of Scientific & Technical Information Technical Reports

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  • December 1, 1998

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

Description Last Updated

  • Nov. 3, 2016, 1:23 p.m.

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Beiu, V. & Moore, K.R. On analog implementations of discrete neural networks, article, December 1, 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc707529/: accessed June 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.