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Learning and System Modeling via Hamiltonian Neural Networks

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Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

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Abstract

Hamiltonian Neural Networks based orthogonal filters are universal signal processors. The structure of such processors rely on family of Hurwitz-Radon matrices. To illustrate, we propose in this paper a procedure of nonlinear mapping synthesis. Hence, we propose here system modeling and learning architectures which are suitable for very large scale implementations.

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References

  1. Poggio, T., Smale, S.: The Mathematics of Learning: Dealing with Data. Notices of the AMS 5, 537–544 (2003)

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  2. Sienko, W., Citko, W.: On Very Large Scale Hamiltonian Neural Nets. In: Rutkowski, L., Kacprzyk (eds.) Neural Networks and Soft Computing, Springer, Heidelberg (2003)

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  3. Sienko, W., Citko, W., Wilamowski, B.: Hamiltonian Neural Nets as a Universal Signal Processor. In: The 28th Annual Conference of the IEEE Industrial Electronics Society, Seville (2002) SF 007388

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  4. Eckmann, B.: Topology, Algebra, Analysis-Relations and Missing Links. Notices of the AMS 5, 520–527 (1999)

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  5. Sienko, W., Citko, W.: Quantum Signal Processing via Hamiltonian Neural Networks. In: Dubois, D.M. (ed.) To be published in International Journal of Computing Anticipatory Systems, vol. 14-16, CHAOS, Liege (2004)

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Author information

Authors and Affiliations

  1. Dept. Electrical Engineering, Maritime Academy, Morska 83, 81-455, Gdynia, Poland

    Wieslaw Sienko & Wieslaw Citko

  2. Dept. Electrical Engineering, Technical University of Koszalin, 75-411, Koszalin, Poland

    Dariusz Jakóbczak

Authors
  1. Wieslaw Sienko
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  2. Wieslaw Citko
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  3. Dariusz Jakóbczak
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. German Research Center of Artificial Intelligence (DFKI), Germany

    Jörg H. Siekmann

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA

    Lotfi A. Zadeh

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© 2004 Springer-Verlag Berlin Heidelberg

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Sienko, W., Citko, W., Jakóbczak, D. (2004). Learning and System Modeling via Hamiltonian Neural Networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_36

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  • DOI: https://doi.org/10.1007/978-3-540-24844-6_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

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