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A novel augmented Lyapunov functional for the stability analysis of delayed neural networks

机译：延迟神经网络稳定性分析的新型增强Lyapunov功能

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摘要

This paper investigates the stability of neural networks with a time-varying delay. Based on the good effectiveness of the augmented Lyapunov-Krasovskii functional (LKF), some useful integral vectors are summarized and used to construct single integral terms with augmented quadratic integrand so as to develop a novel augmented LKF candidate. Then an extended reciprocally convex matrix inequality and an auxiliary function-based inequality are utilized to estimate the derivative of the LKF. As a result, an improved stability criterion is established. Finally, the advantage of proposed method is demonstrated by a numerical example.
机译：本文研究了神经网络与时变延迟的稳定性。基于增强Lyapunov-Krasovskii功能（LKF）的良好效果，总结了一些有用的积分矢量，并用于构建具有增强二次积分的单个积分术语，以开发一个新的增强LKF候选人。然后利用延长的互换凸矩阵和基于辅助功能的不等式来估计LKF的衍生物。结果，建立了改进的稳定性标准。最后，通过数值示例对所提出的方法的优点进行说明。

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