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Non-fragile synchronisation of mixed delayed neural networks with randomly occurring controller gain fluctuations

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  • M. Syed Ali
  • N. Gunasekaran
  • R. Agalya
  • Young Hoon Joo

Abstract

This study is concerned with the problem of non fragile synchronisation of mixed delayed neural networks with randomly occurring controller gain fluctuations. By using a novel mathematical approach and considering the neuron activation functions, improved delay-dependent stability results are formulated in terms of linear matrix inequalities (LMIs). An augmented new Lyapunov-Krasovskii functional (LKF) that contains double and triple integral terms is constructed to ensure the asymptotic stability of the error system which guarantees the master system synchronise with the slave system. Finally, numerical examples are provided to show the effectiveness of the proposed theoretical results.

Suggested Citation

  • M. Syed Ali & N. Gunasekaran & R. Agalya & Young Hoon Joo, 2018. "Non-fragile synchronisation of mixed delayed neural networks with randomly occurring controller gain fluctuations," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(16), pages 3354-3364, December.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:16:p:3354-3364
    DOI: 10.1080/00207721.2018.1540730
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    Cited by:

    1. Bei Zhang & Yonghui Xia & Lijuan Zhu & Haidong Liu & Longfei Gu, 2019. "Global Stability of Fractional Order Coupled Systems with Impulses via a Graphic Approach," Mathematics, MDPI, vol. 7(8), pages 1-10, August.
    2. Fan, Gaofeng & Ma, Yuechao, 2023. "Fault-tolerant fixed/preassigned-time synchronization control of uncertain singularly perturbed complex networks with time-varying delay and stochastic disturbances," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

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