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Bounded real lemmas and exponential H∞ control for memristor-based neural networks with unbounded time-varying delays

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  • Meng, Xianhe
  • Zhang, Xian
  • Wang, Yantao

Abstract

This paper focuses on developing a bounded real lemma (BRL) and designing a state-feedback controller which guarantees a prescribed H∞ performance level for a class of memristor-based neural networks (MNNs) with unbounded time-varying delays. Firstly, a BRL for MNNs is presented by taking a new approach based on system solutions. This approach requires neither transformation of the model nor construction of Lyapunov–Krasovskii functionals, thereby reducing computational effort and complexity. In addition, the obtained BRL contains only a few simple inequalities, which can be easily solved by using MATLAB. Secondly, the condition for the existence of exponential H∞ controller is given based on the obtained BRL. Finally, two simulation examples demonstrate the validity of the theoretical results.

Suggested Citation

  • Meng, Xianhe & Zhang, Xian & Wang, Yantao, 2023. "Bounded real lemmas and exponential H∞ control for memristor-based neural networks with unbounded time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 210(C), pages 66-81.
    • Handle: RePEc:eee:matcom:v:210:y:2023:i:c:p:66-81
    DOI: 10.1016/j.matcom.2023.03.014
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    References listed on IDEAS

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