Fixed-time control of switched memristor-based BAM neural networks with time-varying delays

Ziqing Yuan; Zuowei Cai; Ziqing Yuan; Zuowei Cai

doi:10.3934/era.2025305

Electronic Research Archive

2025, Volume 33, Issue 11: 6922-6951. doi: 10.3934/era.2025305

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Fixed-time control of switched memristor-based BAM neural networks with time-varying delays

Ziqing Yuan ^{1
,
,},
Zuowei Cai ²

1.
Department of Applied Mathematics, Huaihua University, Huaihua 418000, China
2.
College of Information Science and Engineering, Hunan Women's University, Changsha 410002, China

Received: 14 September 2025 Revised: 31 October 2025 Accepted: 11 November 2025 Published: 17 November 2025

This study investigates the prespecified-/finite-time stability for a class of memristor-based BAM neural networks (MBAMNNs) with discontinuity. By C-regular Lyapunov approach stability theories, some criteria are established to ensure that the switched MBAMNNs can achieve prespecified-/finite-time stabilization, which are independent of the initial states. Finally, some numerical examples demonstrate the effectiveness of the proposed criteria. This work provides a theoretical foundation for precise temporal control in complex neural networks.
- BAM neural networks,
- prespecified-time stability,
- finite-time stability,
- external input controller
Citation: Ziqing Yuan, Zuowei Cai. Fixed-time control of switched memristor-based BAM neural networks with time-varying delays[J]. Electronic Research Archive, 2025, 33(11): 6922-6951. doi: 10.3934/era.2025305

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Abstract

This study investigates the prespecified-/finite-time stability for a class of memristor-based BAM neural networks (MBAMNNs) with discontinuity. By C-regular Lyapunov approach stability theories, some criteria are established to ensure that the switched MBAMNNs can achieve prespecified-/finite-time stabilization, which are independent of the initial states. Finally, some numerical examples demonstrate the effectiveness of the proposed criteria. This work provides a theoretical foundation for precise temporal control in complex neural networks.

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