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First-Order 2D Cellular Neural Networks Investigation and Learning

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Parallel Computing Technologies (PaCT 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2127))

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Abstract

In this paper first order 2D cellular neural networks (CNN’s) with homogeneous weight structure are investigated. It is proved that all CNN’s are divided into equivalence classes by respect to formed patterns properties. The method of learning first order CNN is proposed, which allows to find the parameters of CNN weight template if an example of stable state is given.

Supported by RFBR, grants 00-01-00026, 01-01-06261

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References

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Author information

Authors and Affiliations

  1. Supercomputer Software Department, ICMMG of SB RAS, Pr. Lavrentieva, 6, Novosibirsk, 630090, Russia

    Sergey Pudov

Authors
  1. Sergey Pudov
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Editor information

Editors and Affiliations

  1. Supercomputer Software Department, ICMand MG SB RAS, pr. Lavrentiev 6, 630090, Novosibirsk, Russia

    Victor Malyshkin

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© 2001 Springer-Verlag Berlin Heidelberg

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Pudov, S. (2001). First-Order 2D Cellular Neural Networks Investigation and Learning. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2001. Lecture Notes in Computer Science, vol 2127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44743-1_9

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  • DOI: https://doi.org/10.1007/3-540-44743-1_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42522-9

  • Online ISBN: 978-3-540-44743-6

  • eBook Packages: Springer Book Archive

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