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Design of sampled-data control for multiple-time delayed generalised neural networks based on delay-partitioning approach

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  • M. Syed Ali
  • N. Gunasekaran
  • B. Aruna

Abstract

In this paper, we addressed the problem of stability analysis for a class of generalised mixed delayed neural networks by delay-partitioning approach. A novel integral inequality is developed by employing Wirtinger's integral inequality and Leibniz–Newton formula. By constructing an augmented Lyapunov–Krasovskii functional with triple and quadruple integral terms and using some standard integral inequality techniques, asymptotic stability criterion is obtained to the concerned neural networks. By converting the sampling period into a bounded time-varying delays, the error dynamics of the considered generalised neural networks are derived in terms of a dynamic system with sampling. Finally, numerical examples are given to show that the proposed method is less conservative than existing ones.

Suggested Citation

  • M. Syed Ali & N. Gunasekaran & B. Aruna, 2017. "Design of sampled-data control for multiple-time delayed generalised neural networks based on delay-partitioning approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(13), pages 2794-2810, October.
    • Handle: RePEc:taf:tsysxx:v:48:y:2017:i:13:p:2794-2810
    DOI: 10.1080/00207721.2017.1344891
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    Cited by:

    1. Thoiyab, N. Mohamed & Muruganantham, P. & Zhu, Quanxin & Gunasekaran, Nallappan, 2021. "Novel results on global stability analysis for multiple time-delayed BAM neural networks under parameter uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Xiangshan Kong & Qilong Sun & Haitao Li, 2022. "Survey on Mathematical Models and Methods of Complex Logical Dynamical Systems," Mathematics, MDPI, vol. 10(20), pages 1-17, October.

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