This paper deals with the extended dissipativity and non-fragile synchronization of delayed recurrent neural networks (RNNs) with multiple time-varying delays and sampled-data control. A suitable Lyapunov-Krasovskii Functional (LKF) is built up to prove the quadratically stable and extended dissipativity condition of delayed RNNs using Jensen inequality and limited Bessel-Legendre inequality approaches. A non-fragile sampled-data approach is applied to investigate the problem of neural networks with multiple time-varying delays, which ensures that the master system synchronizes with the slave system and is designed with respect to the solutions of Linear Matrix Inequalities (LMIs). The effectiveness of the suggested approach is established by providing suitable simulations using MATLAB LMI control toolbox. Finally, numerical examples and comparative results are provided to illustrate the adequacy of the planned control scheme.

中文翻译：

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具有时变时滞的递归神经网络通过采样数据控制的扩展耗散性和非脆弱同步

本文讨论了具有多个时变时滞和采样数据控制的延迟递归神经网络（RNN）的扩展耗散性和非脆弱同步。建立了合适的Lyapunov-Krasovskii泛函（LKF），以使用Jensen不等式和有限的Bessel-Legendre不等式方法证明延迟RNN的二次稳定和扩展耗散性条件。应用非脆弱采样数据方法来研究具有多个时变时延的神经网络问题，该方法可确保主系统与从系统同步，并针对线性矩阵不等式（LMI）的解决方案进行设计。建议的方法的有效性通过使用MATLAB LMI控制工具箱提供适当的仿真来确定。最后，