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<p>For a class of memristive neural networks (MNNs) with multiple links and multiple leakage delays (ML-MLDs), the issue of global exponential input-to-state stability (GE-ISS) analysis is investigated. A direct analysis method based on the GE-ISS definition is proposed to give new criteria of GE-ISS for MNNs with ML-MLDs. The method does not involve model transformation or construction of Lyapunov–Krasovskii functional. The obtain criteria are made up of several scalar inequalities, which can be easily verified. Finally, numerical examples are provided to demonstrate the feasibility of the derived GE-ISS criteria. These findings are crucial for advancing secure communication, biological network modeling, and other applications. It should be mentioned specially that the GE-ISS analysis issue of MNNs with ML-MLDs is solved for the first time.</p>

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Sufficient conditions for input-to-state global exponential stability of multiple link memristive neural networks with multiple leakage delays

  • 期刊论文

  • 发表日期：

    2025年8月27日

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  • Yunxiao Jia,
  • Shasha Xiao,
  • Xian Zhang

摘要

For a class of memristive neural networks (MNNs) with multiple links and multiple leakage delays (ML-MLDs), the issue of global exponential input-to-state stability (GE-ISS) analysis is investigated. A direct analysis method based on the GE-ISS definition is proposed to give new criteria of GE-ISS for MNNs with ML-MLDs. The method does not involve model transformation or construction of Lyapunov–Krasovskii functional. The obtain criteria are made up of several scalar inequalities, which can be easily verified. Finally, numerical examples are provided to demonstrate the feasibility of the derived GE-ISS criteria. These findings are crucial for advancing secure communication, biological network modeling, and other applications. It should be mentioned specially that the GE-ISS analysis issue of MNNs with ML-MLDs is solved for the first time.