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A new approach based on system solutions for passivity analysis of discrete-time memristor-based neural networks with time-varying delays

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  • Tu, Kairong
  • Xue, Yu
  • Zhang, Xian

Abstract

This paper focuses on the passivity analysis of a class of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, a novel sufficient condition composing several simple linear scalar inequalities is given to guarantee the passivity of DTMBNNs with unbounded time-varying delays. The obtained sufficient passivity condition is based on system solutions, and the proposed system solutions-based approach can reduce computational complexity and workload. Secondly, the sufficient passivity condition for DTMBMMs with bounded time-varying delays is also obtained. Finally, the effectiveness of the theoretical results is verified through two simulation examples.

Suggested Citation

  • Tu, Kairong & Xue, Yu & Zhang, Xian, 2024. "A new approach based on system solutions for passivity analysis of discrete-time memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 469(C).
    • Handle: RePEc:eee:apmaco:v:469:y:2024:i:c:s0096300324000237
    DOI: 10.1016/j.amc.2024.128551
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