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Stability and synchronization of fractional-order complex-valued neural networks with time delay: LMI approach

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Abstract

In this paper, we investigate the problem of stability and synchronization of fractional-order complex-valued neural networks with time delay. By using Lyapunov–Krasovskii functional approach, some linear matrix inequality (LMI) conditions are proposed to ensure that the equilibrium point of the addressed neural networks is globally Mittag–Leffler stable. Moreover, some sufficient conditions for projective synchronization of considered fractional-order complex-valued neural networks are derived in terms of LMIs. Finally, two numerical examples are given to demonstrate the effectiveness of our theoretical results.

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Authors and Affiliations

  1. Department of Mathematics, Bharathiar University, Coimbatore, 641 046, Tamil Nadu, India

    K. Udhayakumar & R. Rakkiyappan

  2. Department of Mathematical Sciences, College of Science, UAE University, 15551, Al-Ain, United Arab Emirates

    G. Velmurugan

Authors
  1. K. Udhayakumar
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  2. R. Rakkiyappan
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  3. G. Velmurugan
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Corresponding author

Correspondence to R. Rakkiyappan.

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Udhayakumar, K., Rakkiyappan, R. & Velmurugan, G. Stability and synchronization of fractional-order complex-valued neural networks with time delay: LMI approach. Eur. Phys. J. Spec. Top. 226, 3639–3655 (2017). https://doi.org/10.1140/epjst/e2018-00066-0

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  • DOI: https://doi.org/10.1140/epjst/e2018-00066-0

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