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

Kairong Tu, Yu Xue and Xian Zhang

Applied Mathematics and Computation, 2024, vol. 469, issue C

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.

Keywords: Discrete-time memristor-based neural networks; System solutions-based approach; Passivity analysis; Time-varying delays (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:469:y:2024:i:c:s0096300324000237

DOI: 10.1016/j.amc.2024.128551

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