The essential objective of this research article is to investigate stability issue of neutral-type Cohen–Grossberg neural networks involving multiple time delays in states of neurons and multiple neutral delays in time derivatives of states of neurons in the network. By exploiting a modified and improved version of a previously introduced Lyapunov functional, a new sufficient stability criterion is obtained for global asymptotic stability of Cohen–Grossberg neural networks of neutral-type possessing multiple delays. The proposed new stability condition does not involve the time and neutral delay parameters. The obtained stability criterion is totally dependent on the system elements of Cohen–Grossberg neural network model. Moreover, the validity of this novel global asymptotic stability condition may be tested by only checking simple appropriate algebraic equations established within the parameters of the considered neutral-type neural network. In addition, an instructive numerical example is presented to indicate the advantages of our proposed stability result over the existing literature results obtained for stability of various classes of neutral-type neural networks having multiple delays.

这篇研究文章的基本目的是研究中性型Cohen-Grossberg神经网络的稳定性问题，该神经网络涉及神经元状态的多个时间延迟和网络中神经元状态的时间导数的多个中性延迟。通过利用先前引入的Lyapunov泛函的修改和改进版本，为具有多个延迟的中立型Cohen-Grossberg神经网络的全局渐近稳定性获得了一个新的充分稳定性准则。所提出的新的稳定性条件不涉及时间和中性延迟参数。所获得的稳定性标准完全取决于Cohen-Grossberg神经网络模型的系统元素。此外，仅通过检查在所考虑的中性型神经网络的参数内建立的简单适当的代数方程，即可测试这种新型全局渐近稳定性条件的有效性。此外，还提供了一个说明性的数值示例，以表明我们提出的稳定性结果相对于现有文献结果的优势，该文献为各种具有多个延迟的中立型神经网络的稳定性提供了证据。