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The Analytical Analysis of Hopfield Neuron Parameters by the Application of Special Trans Function Theory

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Adaptive and Natural Computing Algorithms

Abstract

The subject of the theoretical analysis presented in the paper is Hopfield neuron electronic model modification based upon capacitor replacement with an inverse polarized diode. The modified neuron parameters have been analytically analyzed by application of the Special Trans Function Theory (STFT). The obtained results are presented numerically and graphically.

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References

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Montenegro, Montenegro

    S. M. Perovich

  2. Faculty of Maritime Studies, University of Montenegro, Montenegro

    S. I. Bauk & N. Konjevic

Authors
  1. S. M. Perovich
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  2. S. I. Bauk
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  3. N. Konjevic
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Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Bernardete Ribeiro

  2. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

  3. Fakulteta za Računalništvo in Informatiko, Univerza v Ljubljani, Ljubljana, Slovenia

    Andrej Dobnikar

  4. Equipe Universitaire de Recherche en Informatique de Saint-Etienne (Groupe de Recherche de Roanne) Institut Universitaire de Technologie de Roanne, Université Jean Monnet, Saint-Etienne, France

    David W. Pearson

  5. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

    Nigel C. Steele

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© 2005 Springer-Verlag/Wien

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Perovich, S.M., Bauk, S.I., Konjevic, N. (2005). The Analytical Analysis of Hopfield Neuron Parameters by the Application of Special Trans Function Theory. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_9

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  • DOI: https://doi.org/10.1007/3-211-27389-1_9

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-24934-5

  • Online ISBN: 978-3-211-27389-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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