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Neural networks of the hopfield type

  • Neural Modeling (Biophysical and Structural Models)
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Foundations and Tools for Neural Modeling (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1606))

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Abstract

The set of the fixed points of the Hopfield type network is under investigation. The connection matrix of the network is constructed according the Hebb rule from the set of memorized patterns which are treated as distorted copies of the standard-vector. It is found that the dependence of the set of the fixed points on the value of the distortion parameter can be described analytically. The obtained results are interpreted in the terms of neural networks and the Ising model.

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References

  1. J.J. Hopfield. Proc. Natl. Acad. Sci. USA, 1982, 79, p.2554.

    Article  MathSciNet  Google Scholar 

  2. E. Domany, J.L. van Hemmen and K. Schulten (Eds.). Models of neural networks. Berlin, Springer-Verlag, 1991.

    MATH  Google Scholar 

  3. H. Rieger, U. Blasum. Ground state properties of solid-on-solid models with disordered substrates. Cond-mat/9608136, 1996.

    Google Scholar 

  4. N. Sourlas. Europhys. Letters, 1994, 25, p.159.

    Article  MathSciNet  Google Scholar 

  5. G. Parisi. Attractor Neural Networks. Cond-mat/9412030, 1994.

    Google Scholar 

  6. E.M. Braverman, I.B. Muchnik. The structural methods of the empirical date processing (in russian). Nauka, Moscow, 1983.

    Google Scholar 

  7. L.B. Litinsky. Neural Network World, 1996, 6, p.325.

    Google Scholar 

  8. L.B. Litinsky. Energy functional and fixed points of neural network. Condmat/9706280, 1997. Also in: “Neural Nets. WIRN VIETRI-97. Proceedings...”, M. Marinaro and R. Tagliaferri (Eds.), Springer-Verlag, 1998.

    Google Scholar 

  9. L.B. Litinskii. Theor. and Math. Phys., 1999 (in press).

    Google Scholar 

  10. L.B. Litinskii. Theor. and Math. Phys., 1994, 101, p.1492.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. High Pressure Physics Institute of Russian Academy of Sciences, 142092, Troitsk Moscow region, Russia

    Leonid B. Litinskii

Authors
  1. Leonid B. Litinskii
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José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Litinskii, L.B. (1999). Neural networks of the hopfield type. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098184

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  • DOI: https://doi.org/10.1007/BFb0098184

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66069-9

  • Online ISBN: 978-3-540-48771-5

  • eBook Packages: Springer Book Archive

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