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On Very Large Scale Hamiltonian Neural Nets

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Neural Networks and Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 19))

Abstract

This paper presents how to design very large scale lossless neural nets (LONN), which can be used as Walsh-spectrum analyzer. This analysis relies on the orthogonality of weight matrix W where W is Hurwitz-Radon matrix. The unique feature of the LONN is the possibility to treat them either as algorithms or as Hamiltonian physical objects (Walsh Transformation Processors).

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References

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Gdynia Maritime Academy, 81-225, Morska 83, Gdynia, Poland

    Wieslaw Sienko & Wieslaw Citko

Authors
  1. Wieslaw Sienko
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  2. Wieslaw Citko
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Technical University Częstochowa, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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© 2003 Springer-Verlag Berlin Heidelberg

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Sienko, W., Citko, W. (2003). On Very Large Scale Hamiltonian Neural Nets. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_38

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_38

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0005-0

  • Online ISBN: 978-3-7908-1902-1

  • eBook Packages: Springer Book Archive

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