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The Average Radius of Attraction Basin of Hopfield Neural Networks

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5264))

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Abstract

This paper introduces a derivation of the attraction basin to the Hopfield neural networks and obtains an average radius of the attraction basin, which is a expression of Hamming distance. The average radius of the attraction basin is (N − 1) / 2P. If the average of Hamming distance between the probe pattern and a stored pattern is less than (N − 1) / 2P, the neural network will converge to the stored pattern.

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Author information

Authors and Affiliations

  1. Institute of Advanced Control and Intelligent Information Processing, Henan University, Kaifeng, 475001, China

    Fan Zhang

  2. College of Electronic and Information Engineering, Tianjin University, Tianjin, 300072, China

    Fan Zhang

  3. Computing Center, Henan University, Kaifeng, 475001, China

    Xinhong Zhang

Authors
  1. Fan Zhang
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  2. Xinhong Zhang
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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Zhang, F., Zhang, X. (2008). The Average Radius of Attraction Basin of Hopfield Neural Networks. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_29

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  • DOI: https://doi.org/10.1007/978-3-540-87734-9_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87733-2

  • Online ISBN: 978-3-540-87734-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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