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A modified backpropagation algorithm to tolerate weight errors

  • Neural Nets Simulation, Emulation and Implementation
  • Conference paper
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Biological and Artificial Computation: From Neuroscience to Technology (IWANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1240))

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Abstract

A modified backpropagation algorithm that minimizes the sensitivity to weight errors is presented. Multi-Layer Perceptrons (MLPs) trained with this algorithm are more tolerant to weight deviations compared to those obtained with a classical algorithm while the other performance figures are similar. Thus the algorithm is useful for MLPs that are going to be mapped on a physical implementation that can be affected by weight imprecision.

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Author information

Authors and Affiliations

  1. Dto. Electrónica y Tecnología de Computadores, Universidad de Granada, 18071, Granada, Spain

    Jose L. Bernier, Julio Ortega & Alberto Prieto

Authors
  1. Jose L. Bernier
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  2. Julio Ortega
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  3. Alberto Prieto
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Editor information

José Mira Roberto Moreno-Díaz Joan Cabestany

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© 1997 Springer-Verlag Berlin Heidelberg

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Bernier, J.L., Ortega, J., Prieto, A. (1997). A modified backpropagation algorithm to tolerate weight errors. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032535

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  • DOI: https://doi.org/10.1007/BFb0032535

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63047-0

  • Online ISBN: 978-3-540-69074-0

  • eBook Packages: Springer Book Archive

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