Synchronization of Caputo‐Hadamard Fractional‐Order Competitive Neural Networks With Discrete and Distributed Delays

ABSTRACT This paper investigates the synchronization problem for Caputo–Hadamard fractional‐order competitive neural networks (CHFCNNs) with both discrete and distributed delays. First, a new lemma is established by employing Caputo–Hadamard fractional calculus theory and analytical techniques. Second, an adaptive hybrid controller and a linear controller are designed. By employing inequality techniques, leveraging properties of the Mittag‐Leffler function, and utilizing our established lemma, the criteria for both Mittag‐Leffler synchronization and complete synchronization are derived. Finally, the validity of the theoretical results is verified through numerical examples.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2026-04-14
DOI
https://doi.org/10.1002/mma.70745
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00

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article

Synchronization of Caputo‐Hadamard Fractional‐Order Competitive Neural Networks With Discrete and Distributed Delays

Long Zhang, Jinde Cao, Zhenzhen Zhang, Dandan Li
Mathematical Methods in the Applied Sciences
Neural Networks Stability and Synchronization
article

Synchronization of Caputo‐Hadamard Fractional‐Order Competitive Neural Networks With Discrete and Distributed Delays

Long Zhang, Jinde Cao, Zhenzhen Zhang, Dandan Li
article en

Abstract

ABSTRACT This paper investigates the synchronization problem for Caputo–Hadamard fractional‐order competitive neural networks (CHFCNNs) with both discrete and distributed delays. First, a new lemma is established by employing Caputo–Hadamard fractional calculus theory and analytical techniques. Second, an adaptive hybrid controller and a linear controller are designed. By employing inequality techniques, leveraging properties of the Mittag‐Leffler function, and utilizing our established lemma, the criteria for both Mittag‐Leffler synchronization and complete synchronization are derived. Finally, the validity of the theoretical results is verified through numerical examples.

Mathematical Methods in the Applied Sciences
Changji University (CN), Southeast University (CN), Xinjiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 15%
Neural Networks Stability and Synchronization
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