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New results on exponential input-to-state stability analysis of memristor based complex-valued inertial neural networks with proportional and distributed delays

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  • Iswarya, M.
  • Raja, R.
  • Cao, J.
  • Niezabitowski, M.
  • Alzabut, J.
  • Maharajan, C.

Abstract

The present work accumulates the Exponential input-to-state stability (EISS) criteria of memristor based delayed complex-valued neural networks (DMCNN) associated with an inertial term and time-varying delays. Here two varieties of time-varying delays are provided, namely proportional and distributed delays. In this study, the delayed memristor neural networks (MNN) is constructed on the basis of second order complex-valued space. In addition, the sufficient conditions are proposed to ensure the EISS by using the combination of non-smooth analysis, set-valued maps, Lyapunov-Krasovskii functional having double integral terms and Kirchhoff’s matrix tree theorem, moreover we employ Cauchy-Schwarz inequality & some inequality techniques. At the end of this work, the hypothesis has been established with an illustrative example along with the simulations.

Suggested Citation

  • Iswarya, M. & Raja, R. & Cao, J. & Niezabitowski, M. & Alzabut, J. & Maharajan, C., 2022. "New results on exponential input-to-state stability analysis of memristor based complex-valued inertial neural networks with proportional and distributed delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 440-461.
    • Handle: RePEc:eee:matcom:v:201:y:2022:i:c:p:440-461
    DOI: 10.1016/j.matcom.2021.01.020
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