Quantized Projective Synchronization of Delayed Fractional Order Uncertain Quaternion‐Valued Neural Networks Via Direct Method

ABSTRACT This paper treats the projective synchronization (PS) in finite time for a class of delayed fractional order uncertain quaternion‐valued neural networks (DFOUQVNNs) based on event triggered quantized control (ETQC). In addition, to consider more general models, uncertainty and time delay terms are introduced into the FOQVNNs. Different from using decomposition method, the considered model is treated as a single entity. By designing a suitable Lyapunov function and applying inequality skills, sufficient criteria are derived to ensure PS in finite time of DFOUQVNNs. Furthermore, the setting time is estimated and the Zeno behavior of the system is excluded under the proposed scheme. Finally, the effectiveness of the theoretical results is validated by using a numerical example.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-08-03
DOI
https://doi.org/10.1002/mma.70019
Citations
1
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.49

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article

Quantized Projective Synchronization of Delayed Fractional Order Uncertain Quaternion‐Valued Neural Networks Via Direct Method

Jinde Cao, Chunlin Sha, Hongyong Zhao, Wei Wei Zhang
1 citations
Mathematical Methods in the Applied Sciences
Neural Networks Stability and Synchronization
1.49
article

Quantized Projective Synchronization of Delayed Fractional Order Uncertain Quaternion‐Valued Neural Networks Via Direct Method

Jinde Cao, Chunlin Sha, Hongyong Zhao, Wei Wei Zhang
article en
1 citations

Abstract

ABSTRACT This paper treats the projective synchronization (PS) in finite time for a class of delayed fractional order uncertain quaternion‐valued neural networks (DFOUQVNNs) based on event triggered quantized control (ETQC). In addition, to consider more general models, uncertainty and time delay terms are introduced into the FOQVNNs. Different from using decomposition method, the considered model is treated as a single entity. By designing a suitable Lyapunov function and applying inequality skills, sufficient criteria are derived to ensure PS in finite time of DFOUQVNNs. Furthermore, the setting time is estimated and the Zeno behavior of the system is excluded under the proposed scheme. Finally, the effectiveness of the theoretical results is validated by using a numerical example.

Mathematical Methods in the Applied SciencesVol. 48(16)
Anqing Normal University (CN), Southeast University (CN), Ministry of Industry and Information Technology (CN), Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China, Natural Science Foundation of Anhui Province, Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province
Openalex Percentile: Top 15%
Neural Networks Stability and Synchronization
1.49
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Quantized Projective Synchronization of Delayed Fractional Order Uncertain Quaternion‐Valued Neural Networks Via Direct Method — Jinde Cao, Chunlin Sha, et al. · Mathematical Methods in the Applied Sciences (2025) | TGRS Research Map | TGRS