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state estimation of generalised neural networks with interval time-varying delays

Author

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  • R. Saravanakumar
  • M. Syed Ali
  • Jinde Cao
  • He Huang

Abstract

This paper focuses on studying the H∞ state estimation of generalised neural networks with interval time-varying delays. The integral terms in the time derivative of the Lyapunov–Krasovskii functional are handled by the Jensen’s inequality, reciprocally convex combination approach and a new Wirtinger-based double integral inequality. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with H∞ performance. The proposed conditions are represented by linear matrix inequalities. Optimal H∞ norm bounds are obtained easily by solving convex problems in terms of linear matrix inequalities. The advantage of employing the proposed inequalities is illustrated by numerical examples.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:16:p:3888-3899
    DOI: 10.1080/00207721.2015.1135359
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    References listed on IDEAS

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    1. Liang, Jinling & Cao, Jinde, 2006. "A based-on LMI stability criterion for delayed recurrent neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 28(1), pages 154-160.
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    Cited by:

    1. Maharajan, C. & Raja, R. & Cao, Jinde & Rajchakit, G. & Tu, Zhengwen & Alsaedi, Ahmed, 2018. "LMI-based results on exponential stability of BAM-type neural networks with leakage and both time-varying delays: A non-fragile state estimation approach," Applied Mathematics and Computation, Elsevier, vol. 326(C), pages 33-55.
    2. 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.
    3. Li, Hui & Kao, Yonggui & Li, Hong-Li, 2021. "Globally β-Mittag-Leffler stability and β-Mittag-Leffler convergence in Lagrange sense for impulsive fractional-order complex-valued neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).

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