Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions

Gao, Panqing; Ye, Renyu; Zhang, Hai; Stamova, Ivanka; Cao, Jinde

doi:10.1016/j.matcom.2023.11.005

Mathematics and Computers in Simulation

2024

DOI: 10.1016/j.matcom.2023.11.005

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Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions

Panqing Gao,

Renyu Ye,

Hai Zhang

et al.

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2024

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Cited by 3 publications

References 46 publications

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Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Khan,

Chaudhary,

Khan

et al. 2024

Chaos, Solitons & Fractals

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Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Khan,

Chaudhary,

Khan

et al. 2024

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

Finite-time synchronization of proportional delay memristive competitive neural networks

Han,

Zhou

2024

Neurocomputing

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Adaptive Synchronization of Fractional-Order Uncertain Complex-Valued Competitive Neural Networks under the Non-Decomposition Method

Chen,

Luo,

Yang

et al. 2024

Fractal Fract

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This paper is devoted to the study of adaptive synchronization for fractional-order uncertain complex-valued competitive neural networks (FOUCVCNNs) using the non-decomposition method. Firstly, a new network model named FOUCVCNNs is proposed, which is not separated into two real-valued subsystems in order to keep its intrinsic speciality. In addition, a novel adaptive controller is designed to reduce the cost of control. Meanwhile, with the help of fractional Lyapunov theory, 1-norm analysis framework and inequality techniques, several effective synchronization criteria for FOUCVCNNs are obtained by constructing an appropriate Lyapunov function. Finally, the reliability of the results obtained is tested through numerical simulation.

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Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions

Cited by 3 publications

References 46 publications

Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation

Finite-time synchronization of proportional delay memristive competitive neural networks

Adaptive Synchronization of Fractional-Order Uncertain Complex-Valued Competitive Neural Networks under the Non-Decomposition Method

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