This paper considers the drive-response synchronization in finite-time and fixed-time of inertial neural networks with time-varying and distributed delays (mixed delays). First, by constructing a proper variable substitution, the original inertial neural networks can be rewritten as a first-order differential system. Second, by constructing Lyapunov functions and using differential inequalities, some new and effective criteria are obtained for ensuring the finite-time synchronization. Finally, three numerical examples are also given at the end of this paper to show the effectiveness of the results.

中文翻译：

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具有混合延迟的惯性神经网络的有限时间和固定时间同步

本文考虑具有时变和分布时滞（混合时滞）的惯性神经网络在有限时间和固定时间的驱动响应同步。首先，通过构造适当的变量替换，可以将原始惯性神经网络重写为一阶微分系统。其次，通过构造李雅普诺夫函数并利用微分不等式，获得了一些新的有效准则来确保有限时间同步。最后，本文最后还给出了三个数值示例，以证明结果的有效性。