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On permutation symmetries of hopfield model neural network

Author

Listed:
  • Jiyang Dong
  • Shenchu Xu
  • Zhenxiang Chen
  • Boxi Wu

Abstract

Discrete Hopfield neural network (DHNN) is studied by performing permutation operations on the synaptic weight matrix. The storable patterns set stored with Hebbian learning algorithm in a network without losing memories is studied, and a condition which makes sure all the patterns of the storable patterns set have a same basin size of attraction is proposed. Then, the permutation symmetries of the network are studied associating with the stored patterns set. A construction of the storable patterns set satisfying that condition is achieved by consideration of their invariance under a point group.

Suggested Citation

  • Jiyang Dong & Shenchu Xu & Zhenxiang Chen & Boxi Wu, 2001. "On permutation symmetries of hopfield model neural network," Discrete Dynamics in Nature and Society, Hindawi, vol. 6, pages 1-8, January.
    • Handle: RePEc:hin:jnddns:458198
    DOI: 10.1155/S1026022601000139
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