This article mainly explores a class of inertial proportional delayed neural networks. Abstaining reduced order strategy, a novel approach involving differential inequality technique and Lyapunov function fashion is presented to open out that all solutions of the considered system with their derivatives are convergent to zero vector, which refines some previously known research. Moreover, an example and its numerical simulations are given to display the exactness of the proposed approach.

中文翻译：

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惯性比例延迟神经网络的收敛性分析

本文主要探讨一类惯性比例延迟神经网络。通过减少降阶策略，提出了一种涉及微分不等式技术和Lyapunov函数形式的新颖方法，以揭示考虑系统及其导数的所有解都收敛于零向量，从而完善了一些先前已知的研究。此外，给出了一个例子及其数值模拟，以显示所提出方法的正确性。