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Improved Quasiuniform Stability for Fractional Order Neural Nets with Mixed Delay

Author

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  • Omar Naifar
  • Assaad Jmal
  • A. M. Nagy
  • Abdellatif Ben Makhlouf

Abstract

In the present paper, a quasiuniform stability result for fractional order neural networks with mixed delay is developed, based on the generalized Gronwall inequality and the Caputo fractional derivative. Sufficient conditions are derived to ensure the quasiuniform stability of the considered neural nets system. A clarification example is carried out not only to validate the authors’ theoretical results but also to show the superiority of the developed work (in terms of improved stability), compared with other similar works already published in the literature.

Suggested Citation

  • Omar Naifar & Assaad Jmal & A. M. Nagy & Abdellatif Ben Makhlouf, 2020. "Improved Quasiuniform Stability for Fractional Order Neural Nets with Mixed Delay," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-7, November.
    • Handle: RePEc:hin:jnlmpe:8811226
    DOI: 10.1155/2020/8811226
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    Cited by:

    1. Marzban, Hamid Reza, 2022. "A generalization of Müntz-Legendre polynomials and its implementation in optimal control of nonlinear fractional delay systems," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    2. Tabatabaei, S. Sepehr & Dehghan, Mohammad Reza & Talebi, Heidar Ali, 2022. "Real-time prediction of soft tissue deformation; a non-integer order modeling scheme and a practical verification for the theoretical concept," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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