A new chaotic Hopfield neural network and its synthesis via parameter switchings

Juan Li, Feng Liu, Zhi Hong Guan, Tao Li

    Research output: Contribution to journalArticlepeer-review

    25 Scopus citations

    Abstract

    In this paper, we present a new chaotic attractor in Hopfield neural network. Numerical experiments show that the presented Hopfield neural network can display complex dynamics by changing the self-connection weight. Surprisingly, coexistence of a chaotic attractor and a limit cycle is found in this system, which means, the system can exhibit a chaotic attractor or a limit cycle according to different initial values, and this phenomenon is never reported before. We give a rigorous verification of existence of horseshoe chaos by virtue of topological horseshoes theory and estimates of topological entropy in the derived Poincaré maps. Finally, synthesis of the chaotic attractor is studied via parameter switching and a numerical example illustrates the effectiveness of this method.

    Original languageEnglish
    Pages (from-to)33-39
    Number of pages7
    JournalNeurocomputing
    Volume117
    DOIs
    StatePublished - 6 Oct 2013

    Keywords

    • Hopfield neural networks
    • Horseshoe
    • New chaos
    • Parameter switching
    • Synthesis of chaos

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