Synchronization control of a class of fractional-order neutral neural networks with mixed time-varying delays

Abstract This paper focuses on the synchronization control problem of fractional-order neutral neural networks (FONNs) characterized by mixed time-varying delays, which include leakage, transmission, distributed, and neutral delays. First, a novel synchronization control system is designed, which comprises a drive system, a response system, and a feedback controller. Subsequently, conditions for asymptotic synchronization between drive and response systems are established in the form of linear matrix inequalities (LMIs) and proven using Lyapunov functional theory, incorporating a variable matrix weighting approach, and Halanay-type inequality. Finally, four simulation examples are conducted to validate the feasibility and effectiveness of the theoretical analysis, demonstrating clear advantages over previously reported results.

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Publication Details

Journal
Journal of Nonlinear Complex and Data Science
Published
2026-09-21
DOI
https://doi.org/10.1515/jncds-2026-0019
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
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article

Synchronization control of a class of fractional-order neutral neural networks with mixed time-varying delays

Yizhen Wang, Wuhao Zhou, Guoquan Liu, Shumin Zhou
Journal of Nonlinear Complex and Data Science
Neural Networks Stability and Synchronization
article

Synchronization control of a class of fractional-order neutral neural networks with mixed time-varying delays

Yizhen Wang, Wuhao Zhou, Guoquan Liu, Shumin Zhou
article en

Abstract

Abstract This paper focuses on the synchronization control problem of fractional-order neutral neural networks (FONNs) characterized by mixed time-varying delays, which include leakage, transmission, distributed, and neutral delays. First, a novel synchronization control system is designed, which comprises a drive system, a response system, and a feedback controller. Subsequently, conditions for asymptotic synchronization between drive and response systems are established in the form of linear matrix inequalities (LMIs) and proven using Lyapunov functional theory, incorporating a variable matrix weighting approach, and Halanay-type inequality. Finally, four simulation examples are conducted to validate the feasibility and effectiveness of the theoretical analysis, demonstrating clear advantages over previously reported results.

Journal of Nonlinear Complex and Data Science
East China University of Technology (CN)
Openalex Percentile: Top 9%
Neural Networks Stability and Synchronization
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Synchronization control of a class of fractional-order neutral neural networks with mixed time-varying delays — Yizhen Wang, Wuhao Zhou, et al. · Journal of Nonlinear Complex and Data Science (2026) | TGRS Research Map | TGRS