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Extended dissipativity of generalised neural networks including time delays

Author

Listed:
  • R. Saravanakumar
  • Grienggrai Rajchakit
  • M. Syed Ali
  • Young Hoon Joo

Abstract

This article explores the extended dissipativity conditions for generalised neural networks (GNNs) including interval time-varying delays. Extended dissipativity criterions are proposed by making proper Lyapunov–Krasovskii functional. The improved reciprocally convex combination and weighted integral inequality techniques are together applied in main results to establish the new extended dissipativity conditions of delayed GNNs. Finally, the feasibility and superiority of the proposed novel approach is clearly shown by numerical examples.

Suggested Citation

  • R. Saravanakumar & Grienggrai Rajchakit & M. Syed Ali & Young Hoon Joo, 2017. "Extended dissipativity of generalised neural networks including time delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(11), pages 2311-2320, August.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:11:p:2311-2320
    DOI: 10.1080/00207721.2017.1316882
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    References listed on IDEAS

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    1. Hien, Le Van & Trinh, Hieu, 2016. "Exponential stability of time-delay systems via new weighted integral inequalities," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 335-344.
    2. F. El Haoussi & E.H. Tissir & F. Tadeo & A. Hmamed, 2011. "Delay-dependent stabilisation of systems with time-delayed state and control: application to a quadruple-tank process," International Journal of Systems Science, Taylor & Francis Journals, vol. 42(1), pages 41-49.
    3. R. Saravanakumar & M. Syed Ali & Jinde Cao & He Huang, 2016. "state estimation of generalised neural networks with interval time-varying delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(16), pages 3888-3899, December.
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