In this paper, the stability problem of neural networks is addressed by considering time-varying delays. By proposing novel geometry-based negative conditions for the form of quadratic function and constructing new augmented Lyapunov-Krasovskii functionals, a novel stability criterion is derived. Finally, to show the effectiveness of the proposed criterion, several numerical examples are given.

中文翻译：

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使用基于几何的新条件，具有二次函数形式的时变时滞神经网络的改进稳定性准则

本文通过考虑时变时延来解决神经网络的稳定性问题。通过提出二次函数形式的基于几何的新的负条件并构造新的增强的Lyapunov-Krasovskii泛函，得出了一个新的稳定性判据。最后，为了显示所提出准则的有效性，给出了几个数值示例。