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The focus of this paper lies in the state estimation and control of switched neural networks (SNNs) under mode-dependent hybrid dwell time (MDHDT). First, a novel MDHDT switching machanism is constructed based on average dwell time (ADT) and mode-dependent average dwell time (MDADT). Then a mode-dependent time scheduler is designed by introducing fixed period and a mode-dependent maximum integer. Moreover, a stability criterion to guarantee the global uniform exponential stability (GUES) of the switched closed-loop system is given by constructing mode-dependent multiple Lyapunov function. Finally, the authenticity of the obtained conclusion is validated through data simulation.

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State Estimation and Control of Switched Neural Networks with Mode-Dependent Hybrid Dwell Time

  • 会议论文

  • 发表日期：

    2025年

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  • Dan Li,
  • Jinling Wang,
  • Jiarong Li

摘要

The focus of this paper lies in the state estimation and control of switched neural networks (SNNs) under mode-dependent hybrid dwell time (MDHDT). First, a novel MDHDT switching machanism is constructed based on average dwell time (ADT) and mode-dependent average dwell time (MDADT). Then a mode-dependent time scheduler is designed by introducing fixed period and a mode-dependent maximum integer. Moreover, a stability criterion to guarantee the global uniform exponential stability (GUES) of the switched closed-loop system is given by constructing mode-dependent multiple Lyapunov function. Finally, the authenticity of the obtained conclusion is validated through data simulation.