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Neural Processing Letters

Multisynchronization of Delayed Fractional-Order Neural Networks via Average Impulsive Interval

  • 10-11-2023
Published in:
Authors
Xue Wang
Xiaoshuai Ding
Jian Li
Jinde Cao
Published in
Neural Processing Letters | Issue 9/2023

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Abstract

The article delves into the complex dynamics of delayed fractional-order neural networks, focusing on the challenges and solutions for achieving multisynchronization. By leveraging advanced control strategies, such as impulsive control and average impulsive interval methods, the authors provide comprehensive theoretical frameworks and practical examples to demonstrate the effectiveness of their approach. The research addresses the critical issue of parametric uncertainties, enhancing the robustness and applicability of the proposed methods. The article is particularly notable for its rigorous mathematical analysis and innovative applications in neural network synchronization, making it a valuable resource for researchers and practitioners in the field.
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previous article Dynamic Event-Triggered Passive Synchronization for Mode-Dependent Persistent Dwell-Time Switched Neural Networks Subject to DoS Attacks
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Title
Multisynchronization of Delayed Fractional-Order Neural Networks via Average Impulsive Interval
Authors
Xue Wang
Xiaoshuai Ding
Jian Li
Jinde Cao
Publication date
10-11-2023
Publisher
Springer US
Published in
Neural Processing Letters / Issue 9/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-023-11427-6
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