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    A new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks

    Access Status
    Fulltext not available
    Authors
    Xiao, S.
    Lian, H.
    Teo, Kok Lay
    Zeng, H.
    Zhang, X.
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Xiao, S. and Lian, H. and Teo, K.L. and Zeng, H. and Zhang, X. 2018. A new Lyapunov functional approach to sampled-data synchronization control for delayed neural networks. Journal of the Franklin Institute. 355 (17): pp. 8857-8873.
    Source Title
    Journal of the Franklin Institute
    DOI
    10.1016/j.jfranklin.2018.09.022
    ISSN
    0016-0032
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/72102
    Collection
    • Curtin Research Publications
    Abstract

    This paper discusses the problem of synchronization for delayed neural networks using sampled-data control. We introduce a new Lyapunov functional, called complete sampling-interval-dependent discontinuous Lyapunov functional, which can adequately capture sampling information on both intervals from r(t-t¯) to r(tk-t¯) and from r(t-t¯) to r(tk+1-t¯). Based on this Lyapunov functional and an improved integral inequality, less conservative conditions are derived to ensure the stability of the synchronization error system, leading to the fact that the drive neural network is synchronized with the response neural network. The desired sampled-data controller is designed in terms of solutions to linear matrix inequalities. A numerical example is provided to demonstrate that the proposed approaches are effective and superior to some existing ones in the literature.

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