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Adaptive quasi-synchronization analysis for Caputo delayed Cohen–Grossberg neural networks

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  • Zhang, Hai
  • Chen, Xinbin
  • Ye, Renyu
  • Stamova, Ivanka
  • Cao, Jinde

Abstract

The quasi-synchronization (QS) issues for Caputo delayed Cohen–Grossberg neural networks (CGNNs) are discussed in this article. To begin with, a novel lemma is established by constructing suitable fractional differential inequality. Due to the advantages of adaptive control schemes with reducing control cost and having high tracking accuracy, two different adaptive controllers are designed, respectively. Applying the proposed lemma, inequality techniques and Lagrange’s mean value theorem, the conditions of QS are obtained by selecting appropriate Lyapunov functions. Finally, two numerical examples in different dimensions are shown to test the correctness of the gained theorems.

Suggested Citation

  • Zhang, Hai & Chen, Xinbin & Ye, Renyu & Stamova, Ivanka & Cao, Jinde, 2023. "Adaptive quasi-synchronization analysis for Caputo delayed Cohen–Grossberg neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 49-65.
    • Handle: RePEc:eee:matcom:v:212:y:2023:i:c:p:49-65
    DOI: 10.1016/j.matcom.2023.04.025
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    References listed on IDEAS

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