Abstract This paper further investigated the synchronization problem of the chaotic neural networks by utilizing the sampled-data control with actuator saturation. Firstly, an augmented two-side-looped Lyapunov functional including both the states of the error system and their derivative is constructed. Then the Wirtinger-based integral inequality in combination with the improved reciprocally convex matrix inequality is applied to estimate the derivative of the presented Lyapunov functional and improved synchronization criteria are derived. As a result, a state feedback controller based on sampled-data is designed, making the drive system synchronize with the response system. Finally, through the results of the numerical example, the validity and superiority of the proposed methods have been confirmed.

中文翻译：

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用于基于采样数据同步执行器饱和的混沌神经网络的增强型两侧循环 Lyapunov 泛函

摘要 本文进一步研究了混沌神经网络的同步问题，利用执行器饱和的采样数据控制。首先，构造了一个包含误差系统状态及其导数的增强双边环李雅普诺夫泛函。然后，将基于 Wirtinger 的积分不等式与改进的互逆凸矩阵不等式相结合，用于估计所提出的 Lyapunov 函数的导数，并推导出改进的同步准则。因此，设计了基于采样数据的状态反馈控制器，使驱动系统与响应系统同步。最后，通过数值算例的结果，验证了所提方法的有效性和优越性。