This research work conducts an investigation of the stability issues of neutral-type Cohen–Grossberg neural network models possessing discrete time delays in states and discrete neutral delays in time derivatives of neuron states. By setting a new generalized appropriate Lyapunov functional candidate, some novel sufficient conditions are proposed for global asymptotic stability for the considered neural networks of neutral type. This paper exploits some basic properties of matrices in the derivation of the results that establish a set of algebraic mathematical relationships between network parameters of this neural system. A key feature of the obtained stability criteria is to be independent from time and neutral delays. Therefore, the derived results can be easily tested. Moreover, a constructive numerical example is studied to check the verification of presented global stability conditions.

中文翻译：

---

中立型延迟Cohen-Grossberg神经网络稳定性的新结果

这项研究工作对中立型Cohen-Grossberg神经网络模型的稳定性问题进行了调查，该模型具有状态的离散时间延迟和神经元状态的时间导数具有离散的中性延迟。通过设置新的广义适当的Lyapunov函数候选者，为考虑的中性型神经网络的全局渐近稳定性提出了一些新颖的充分条件。本文在推导结果的过程中利用了矩阵的一些基本属性，该结果在该神经系统的网络参数之间建立了一组代数数学关系。获得的稳定性标准的关键特征是独立于时间和中性延迟。因此，可以容易地测试得出的结果。此外，