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Master–Slave Finite-Time Synchronization of Chaotic Fractional-Order Neural Networks under Hybrid Sampled-Data Control: An LMI Approach
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Master–Slave Finite-Time Synchronization of Chaotic Fractional-Order Neural Networks under Hybrid Sampled-Data Control: An LMI Approach
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Journal Article
Master–Slave Finite-Time Synchronization of Chaotic Fractional-Order Neural Networks under Hybrid Sampled-Data Control: An LMI Approach
2025
Overview
In this paper, a hybrid controller with a sampled data control is investigated to achieve finite-time master–slave synchronization of delayed fractional-order neural networks (DFONNs). A Lyapunov-Krasovskii functional is constructed to obtain the sufficient conditions that incorporate delay information. For the first time, the asymptotic stability of the error system is guaranteed in a finite-time using the inequality technique and a sampled-data hybrid controller. The obtained conditions are expressed via linear matrix inequality. Notably, the proposed approach outperforms existing methods, demonstrating improved results in a comparative analysis. An explicit formula is utilized to calculate the settling time, which is significantly influenced by the fractional order$$0<\\beta \\le 1$$0 < β ≤ 1 . The superior performance of the proposed control method is evident, showcasing its effectiveness through numerical simulations and addressing the synchronization problem in DFONNs.
Publisher
Springer Nature B.V
