The convergence properties of a clipped Hopfield network and its application in the design of keystream generator

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journal

21 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)340-348
Journal / PublicationIEEE Transactions on Neural Networks
Volume12
Issue number2
Publication statusPublished - Mar 2001

Abstract

We first present a modified Hopfield network, the clipped Hopfield network, with synaptic weights assigned to three values {-1, 0, +1}. We give the necessary conditions under which a set of 2n binary vectors can be stored as stable points of the network. We show that in the parallel updating mode, for most of the state vectors, the network will always converge to these 2n stable points. We further demonstrate that these 2n stable points can be divided into two groups, the α group and the β group, each with n stable points. It is shown that the basins of attraction of the stable points in the α group are evenly distributed, and the basins of attraction of the stable points in the β group are also evenly distributed. By ways of application, we show that this class of Hopfield network can be used to build a cryptographically secure keystream generator.

Research Area(s)

  • Attraction basins, Convergence property, Cryptography, Hopfield network, Keystream generator