In this paper, a class of inertial neural networks with bounded time-varying delays and unbounded continuously distributed delays are explored by applying non-reduced order method. Based upon differential inequality techniques and Lyapunov function method, a new sufficient condition is presented to ensure all solutions of the addressed model and their derivatives converge to zero vector, which refines some previously known researches. Moreover, a numerical example is provided to illustrate these analytical conclusions.

中文翻译：

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具有时变时滞和连续分布时滞的惯性神经网络的新收敛

本文采用非降阶方法，研究了一类具有时变时滞和无界连续分布时滞的惯性神经网络。基于微分不等式技术和Lyapunov函数方法，提出了一个新的充分条件，以确保寻址模型及其导数的所有解收敛于零向量，从而完善了一些先前的研究。此外，提供了一个数值示例来说明这些分析结论。