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Complex-valued neural networks with time delays in the Lp sense: Numerical simulations and finite time stability

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  • Panda, Sumati Kumari
  • Vijayakumar, Velusamy
  • Nagy, A.M.

Abstract

A discrete fractional-order complex-valued neural network is taken into consideration in the present study. For the existence of the solution of the considered model to be stable in finite time, certain requirements are specified. Our strategy focuses on the use of the recently formulated discrete fractional calculus, mathematical inequalities, Krasnoselskii’s fixed point theorem, and the Arzelà–Ascoli theorem. We present afew numerical examples that demonstrate the theoretical results’ implementation.

Suggested Citation

  • Panda, Sumati Kumari & Vijayakumar, Velusamy & Nagy, A.M., 2023. "Complex-valued neural networks with time delays in the Lp sense: Numerical simulations and finite time stability," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:chsofr:v:177:y:2023:i:c:s0960077923011657
    DOI: 10.1016/j.chaos.2023.114263
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    References listed on IDEAS

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    1. Yang, Rongjiang & Wu, Bo & Liu, Yang, 2015. "A Halanay-type inequality approach to the stability analysis of discrete-time neural networks with delays," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 696-707.
    2. Thabet Abdeljawad, 2013. "On Delta and Nabla Caputo Fractional Differences and Dual Identities," Discrete Dynamics in Nature and Society, Hindawi, vol. 2013, pages 1-12, July.
    3. M. Ganji & F. Gharari, 2018. "The discrete delta and nabla Mittag-Leffler distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(18), pages 4568-4589, September.
    4. Panda, Sumati Kumari & Vijayakumar, Velusamy, 2023. "Results on finite time stability of various fractional order systems," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
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