The significant contribution of this paper is the addressing the stability issue of neutral-type Cohen-Grossberg neural networks possessing multiple time delays in the states of the neurons and multiple neutral delays in time derivative of states of the neurons. By making the use of a novel and enhanced Lyapunov functional, some new sufficient stability criteria are presented for this model of neutral-type neural systems. The obtained stability conditions are completely dependent of the parameters of the neural system and independent of time delays and neutral delays. A constructive numerical example is presented for the sake of proving the key advantages of the proposed stability results over the previously reported corresponding stability criteria for Cohen-Grossberg neural networks of neutral type. Since, stability analysis of Cohen-Grossberg neural networks involving multiple time delays and multiple neutral delays is a difficult problem to overcome, the investigations of the stability conditions of the neutral-type the stability analysis of this class of neural network models has not been given much attention. Therefore, the stability criteria derived in this work can be evaluated as a valuable contribution to the stability analysis of neutral-type Cohen-Grossberg neural systems involving multiple delays.

本文的重要贡献在于解决了中性型Cohen-Grossberg神经网络的稳定性问题，该神经网络在神经元状态中具有多个时间延迟，并且在神经元状态中具有多个中性延迟。通过使用新颖和增强的Lyapunov函数，为该中立型神经系统模型提出了一些新的足够的稳定性准则。所获得的稳定性条件完全取决于神经系统的参数，并且与时间延迟和中性延迟无关。为了证明所提出的稳定性结果的关键优势，优于先前报道的中性型Cohen-Grossberg神经网络的相应稳定性标准，给出了一个建设性的数值示例。以来，涉及多个时滞和多个中立时滞的Cohen-Grossberg神经网络的稳定性分析是一个难以克服的问题，对此类神经网络模型的稳定性分析的中性型稳定性条件的研究尚未引起足够的重视。 。因此，可以将这项工作中得出的稳定性标准评估为对涉及多个延迟的中性型Cohen-Grossberg神经系统的稳定性分析的宝贵贡献。