A study of exponential stability of multiple equilibria in delayed recurrent neural networks

Zhigang Zeng, Wei Xing Zheng

    Research output: Chapter in Book / Conference PaperConference Paperpeer-review

    Abstract

    The problem of exponential stability of multiple equilibria in recurrent neural networks with time-varying delays and concave-convex characteristics is addressed in this paper. The focus is placed upon derivation of some sufficient conditions under which an neural network of order n can have (2k + m - 1)â¿equilibrium points with (k + m)â¿of them having local exponential stability. The new results represent important extensions of the existing results on multistability of delayed recurrent neural networks.
    Original languageEnglish
    Title of host publication2012 IEEE International Symposium on Circuits and Systems: ISCAS 2012: 20-23 May 2012, Seoul, Korea
    PublisherIEEE Xplore
    Pages2083-2086
    Number of pages4
    ISBN (Print)9781467302197
    DOIs
    Publication statusPublished - 2012
    EventIEEE International Symposium on Circuits and Systems -
    Duration: 20 May 2012 → …

    Publication series

    Name
    ISSN (Print)0271-4302

    Conference

    ConferenceIEEE International Symposium on Circuits and Systems
    Period20/05/12 → …

    Keywords

    • associative memory
    • cellular neural networks
    • recurrent neural networks
    • stability analysis

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