In this paper, we discussed about fixed-time synchronization (FXTS) of Inertial Cohen-Grossberg Neural Networks (ICGNNs) with state-dependent delayed impulses. The Lyapunov stability theory and several useful criteria are utilized to make sure that the control parameters are selected in sync with the intended settling time. Two types of the controller are developed in order to guarantee that error-delayed ICGNNs can be synchronized. A sufficient condition for ensuring FXTS for delayed ICGNNs with desynchronization impulses is investigated. In FXTS, the settling time of ICGNNs will have the smallest upper bound and the settling time of desynchronization will have the largest upper bound. We subsequently conducted numerical simulations to substantiate the validity of the proposed discoveries. Finally, we proposed a multi-image encryption algorithm with the help of ICGNNs and presented the statistical analysis to test its efficacy.

中文翻译：

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具有状态相关延迟脉冲控制的惯性Cohen-Grossberg神经网络的固定时间同步及其在多图像加密中的应用

在本文中，我们讨论了具有状态相关延迟脉冲的惯性 Cohen-Grossberg 神经网络 (ICGNN) 的固定时间同步 (FXTS)。利用李亚普诺夫稳定性理论和几个有用的标准来确保控制参数的选择与预期的稳定时间同步。为了保证误差延迟 ICGNN 能够同步，开发了两种类型的控制器。研究了确保具有去同步脉冲的延迟 ICGNN 的 FXTS 的充分条件。在 FXTS 中，ICGNN 的稳定时间将具有最小的上限，而去同步的稳定时间将具有最大的上限。我们随后进行了数值模拟，以证实所提出的发现的有效性。最后，我们在 ICGNN 的帮助下提出了一种多图像加密算法，并进行了统计分析来测试其有效性。