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Influence of self-connection weights on cellular-neural network stability

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Book cover Parallel Computing Technologies (PaCT 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1277))

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Abstract

Cellular-Neural Associative Memory (memory by Hophield with local connection structure) with weight matrix designed by anyone of the existing methods ensuring individual stability of network is concidered. It is studied how self-connection weight values influence the main characteristic of CNAM, namely the strong stability to k-distortions of stored prototypes. Expression for determining the self-connection weight values is obtained, such that provides a maximal strong stability for each prototype. Two strategies are proposed to determine the most acceptable value according to the requiered accuracy. The obtained results are valid not only for CNAM but also for full-connected Hopfield associative memory designed with the help of any learning method.

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References

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Authors and Affiliations

  1. Supercomputer Software Department Computing Center of Siberian Branch, Russian Academy of Science, Pr. Lavrentieva, 6, 630090, Novosibirsk, Russia

    Sergey Pudov

Authors
  1. Sergey Pudov
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Editor information

Victor Malyshkin

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© 1997 Springer-Verlag Berlin Heidelberg

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Pudov, S. (1997). Influence of self-connection weights on cellular-neural network stability. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 1997. Lecture Notes in Computer Science, vol 1277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63371-5_9

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  • DOI: https://doi.org/10.1007/3-540-63371-5_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63371-6

  • Online ISBN: 978-3-540-69525-7

  • eBook Packages: Springer Book Archive

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