H∞ Pinning Synchronization of Delayed Fractional‐Order Markovian Jump Complex Networks With FDI Attacks and Dynamic Quantization

ABSTRACT This article investigates the finite‐horizon pinning synchronization problem for an array of delayed fractional‐order Markovian jump complex networks (MJCNs) with false data injection (FDI) attacks and dynamic quantization. For the sake of decreasing the burden of communication networks, the dynamic quantizer is utilized in a resource‐saving scenario, which exhibits flexibility owing to the incorporation of several tunable parameters. The FDI attacks are modeled by utilizing Bernoulli distributed sequences with the specified probabilities. Subsequently, a quantized pinning control strategy is implemented on the network nodes, aiming to reduce the control costs. Based on the Lyapunov stability theory and linear matrix inequality approach, sufficient conditions are obtained to guarantee the finite‐horizon synchronization of fractional‐order MJCNs against both quantization phenomena and FDI attacks with the hope of higher security. Finally, a simulation example is given to demonstrate the validity of the proposed pinning control strategy.

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Publication Details

Journal
Asian Journal of Control
Published
2026-07-28
DOI
https://doi.org/10.1002/asjc.70208
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.00
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article

H∞ Pinning Synchronization of Delayed Fractional‐Order Markovian Jump Complex Networks With FDI Attacks and Dynamic Quantization

Jun Hu, Shaoting Liu, Yu Chen
Asian Journal of Control
Neural Networks Stability and Synchronization
article

H∞ Pinning Synchronization of Delayed Fractional‐Order Markovian Jump Complex Networks With FDI Attacks and Dynamic Quantization

Jun Hu, Shaoting Liu, Yu Chen
article en

Abstract

ABSTRACT This article investigates the finite‐horizon pinning synchronization problem for an array of delayed fractional‐order Markovian jump complex networks (MJCNs) with false data injection (FDI) attacks and dynamic quantization. For the sake of decreasing the burden of communication networks, the dynamic quantizer is utilized in a resource‐saving scenario, which exhibits flexibility owing to the incorporation of several tunable parameters. The FDI attacks are modeled by utilizing Bernoulli distributed sequences with the specified probabilities. Subsequently, a quantized pinning control strategy is implemented on the network nodes, aiming to reduce the control costs. Based on the Lyapunov stability theory and linear matrix inequality approach, sufficient conditions are obtained to guarantee the finite‐horizon synchronization of fractional‐order MJCNs against both quantization phenomena and FDI attacks with the hope of higher security. Finally, a simulation example is given to demonstrate the validity of the proposed pinning control strategy.

Asian Journal of Control
Harbin University of Science and Technology (CN), Xingtai University (CN)
Openalex Percentile: Top 6%
Neural Networks Stability and Synchronization
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