Exponential/Polynomial Synchronization Analysis of Complex‐Valued Neural Networks With Proportional Delay and Transmission Delay

ABSTRACT This paper investigates the exponential and polynomial synchronization issues of drive–response complex‐valued neural networks (CVNNs) with proportional delay and transmission delay. Firstly, we introduce a definition for polynomial synchronization in CVNNs; then, by constructing Lyapunov function and designing suitable controllers, several delay‐dependent sufficient conditions are proposed to achieve the desired synchronization. In addition, for exponential and polynomial synchronization, their correlation is also derived. Finally, two illustrative examples are proposed to validate the efficacy and demonstrate the superiority of our theoretical findings.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-09-25
DOI
https://doi.org/10.1002/mma.70155
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Exponential/Polynomial Synchronization Analysis of Complex‐Valued Neural Networks With Proportional Delay and Transmission Delay

Zeyuan Huang, Weiqiang Gong, Tengfei Duan, Mali Xing et al.
Mathematical Methods in the Applied Sciences
Neural Networks Stability and Synchronization
article

Exponential/Polynomial Synchronization Analysis of Complex‐Valued Neural Networks With Proportional Delay and Transmission Delay

Zeyuan Huang, Weiqiang Gong, Tengfei Duan, Mali Xing, Qiang Li
article en

Abstract

ABSTRACT This paper investigates the exponential and polynomial synchronization issues of drive–response complex‐valued neural networks (CVNNs) with proportional delay and transmission delay. Firstly, we introduce a definition for polynomial synchronization in CVNNs; then, by constructing Lyapunov function and designing suitable controllers, several delay‐dependent sufficient conditions are proposed to achieve the desired synchronization. In addition, for exponential and polynomial synchronization, their correlation is also derived. Finally, two illustrative examples are proposed to validate the efficacy and demonstrate the superiority of our theoretical findings.

Mathematical Methods in the Applied SciencesVol. 49(1)
Ningbo University (CN), Nanjing University of Finance and Economics (CN), Guangdong University of Technology (CN), Anhui Agricultural University (CN), Anhui Business College (CN), Zhejiang Pharmaceutical College (CN)
Openalex Percentile: Top 70%
Neural Networks Stability and Synchronization
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