Finite-Time Boundedness of Impulsive Delayed Reaction–Diffusion Stochastic Neural Networks

Qi Yao, Tengda Wei, Ping Lin, Linshan Wang (Lead / Corresponding author)

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Considering the impulsive delayed reaction–diffusion stochastic neural networks (IDRDSNNs) with hybrid impulses, the finite-time boundedness (FTB) and finite-time contractive boundedness (FTCB) are investigated in this article. First, a novel delay integral inequality is presented. By integrating this inequality with the comparison principle, some sufficient conditions that ensure the FTB and FTCB of IDRDSNNs are obtained. This study demonstrates that the FTB of neural networks with hybrid impulses can be maintained, even in the presence of impulsive perturbations. And for a system that is not FTB due to impulsive perturbations, achieving FTB is possible through the implementation of appropriate impulsive control and optimization of the average impulsive intervals. In addition, to validate the practicality of our results, three illustrative examples are provided. In the end, these theoretical findings are successfully applied to image encryption.

Original languageEnglish
Article number10443721
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Early online date22 Feb 2024
Publication statusE-pub ahead of print - 22 Feb 2024


  • Delay effects
  • Delays
  • finite-time boundedness (FTB)
  • finite-time contractive boundedness (FTCB)
  • impulses
  • Neural networks
  • Optimization
  • Perturbation methods
  • reaction–diffusion stochastic neural networks
  • Stochastic processes
  • Sufficient conditions

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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