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Further Analysis on Preassigned-time Anti-synchronization of Memristive Inertial BAM Neural Networks
1
School of Mathematics and Statistics, South-Central Minzu University, Wuhan 430074, China
2
School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
* Corresponding Author:
Xiao Zhou,
[email protected]
Volume 1, Issue 2
2025
Received: 13 August 2025
Accepted: 17 September 2025
Published: 19 November 2025
Article Information
Published in
Journal of Nonlinear Dynamics and Applications
Volume/Issue
Volume 1, Issue 2, 2025
Pages
63-75
Cited by
3 (Crossref)
3 (Scopus)
Abstract
This paper studies the preassigned time anti-synchronization control problem of a class of bidirectional associative memory (BAM) neural networks with inertia terms and memristor characteristics. By constructing a novel Lyapunov-Krasovskii function and combining it with the latest fixed-time stability theory, it strictly proves the sufficient conditions for the system to achieve anti-synchronization within the preassigned time. Numerical simulations further verified the effectiveness and superiority of the method, especially demonstrating higher accuracy and flexibility when dealing with high-order dynamics and memristor-based systems.
Graphical Abstract
Keywords
preassigned-time anti-synchronization
non-reduced method
mixed delays
memristive inertial BAM neural networks
Data Availability Statement
Data will be made available on request.
Funding
This work was supported by the National Science Foundation of China under Grant 62476292, and the Fundamental Research Funds for Central University of South-Central Minzu University under Grant CZQ24020.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cited By (4)
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[CrossRef] -
Sunny Singh, Umesh Kumar, Ankit Kumar, Subir Das. Exponential and adaptive lag synchronization of inertial Cohen–Grossberg neural networks with unbounded distributed delays.
Journal of Computational Science, 2026 , 99 .
[CrossRef] -
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[CrossRef] -
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[CrossRef]
* Citation data provided by Crossref Cited-by.
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APA Style
Zhou, X., Hou, J., & Zhang, G. (2025). Further Analysis on Preassigned-time Anti-synchronization of Memristive Inertial BAM Neural Networks. Journal of Nonlinear Dynamics and Applications, 1(2), 63–75. https://doi.org/10.62762/JNDA.2025.473008
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TY - JOUR AU - Zhou, Xiao AU - Hou, Jingrui AU - Zhang, Guodong PY - 2025 DA - 2025/11/19 TI - Further Analysis on Preassigned-time Anti-synchronization of Memristive Inertial BAM Neural Networks JO - Journal of Nonlinear Dynamics and Applications T2 - Journal of Nonlinear Dynamics and Applications JF - Journal of Nonlinear Dynamics and Applications VL - 1 IS - 2 SP - 63 EP - 75 DO - 10.62762/JNDA.2025.473008 UR - https://www.icck.org/article/abs/JNDA.2025.473008 KW - preassigned-time anti-synchronization KW - non-reduced method KW - mixed delays KW - memristive inertial BAM neural networks AB - This paper studies the preassigned time anti-synchronization control problem of a class of bidirectional associative memory (BAM) neural networks with inertia terms and memristor characteristics. By constructing a novel Lyapunov-Krasovskii function and combining it with the latest fixed-time stability theory, it strictly proves the sufficient conditions for the system to achieve anti-synchronization within the preassigned time. Numerical simulations further verified the effectiveness and superiority of the method, especially demonstrating higher accuracy and flexibility when dealing with high-order dynamics and memristor-based systems. SN - 3069-6313 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Zhou2025Further,
author = {Xiao Zhou and Jingrui Hou and Guodong Zhang},
title = {Further Analysis on Preassigned-time Anti-synchronization of Memristive Inertial BAM Neural Networks},
journal = {Journal of Nonlinear Dynamics and Applications},
year = {2025},
volume = {1},
number = {2},
pages = {63-75},
doi = {10.62762/JNDA.2025.473008},
url = {https://www.icck.org/article/abs/JNDA.2025.473008},
abstract = {This paper studies the preassigned time anti-synchronization control problem of a class of bidirectional associative memory (BAM) neural networks with inertia terms and memristor characteristics. By constructing a novel Lyapunov-Krasovskii function and combining it with the latest fixed-time stability theory, it strictly proves the sufficient conditions for the system to achieve anti-synchronization within the preassigned time. Numerical simulations further verified the effectiveness and superiority of the method, especially demonstrating higher accuracy and flexibility when dealing with high-order dynamics and memristor-based systems.},
keywords = {preassigned-time anti-synchronization, non-reduced method, mixed delays, memristive inertial BAM neural networks},
issn = {3069-6313},
publisher = {Institute of Central Computation and Knowledge}
}
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