中文摘要：

在Cohen-Grossberg神经网络设计及实现过程中,非负放大函数条件是很实用的.在网络参数存在摄动的情况下,研究了具有非负放大函数的一类多延时Cohen-Grossberg神经网络的鲁棒渐近稳定性问题.首先通过证明一个有用的引理,建立了渐近稳定性和鲁棒稳定性之间的关系.其次,在不要求激励函数满足严格单调增加和有界性的情况下,通过构造适当的Lyapunov泛函,针对所研究的神经网络模型,基于线性矩阵不等式技术建立了平衡点鲁棒稳定的一个充分判据.仿真结果进一步证明了所得结论的有效性.

英文摘要：

In the process of design and implementation of Cohen-Grossberg neural networks,the nonnegative amplification function condition is very practicable.The robust asymptotic stability of a class of Cohen-Grossberg neural networks with nonnegative amplification function and multiple time delays is investigated in case of parameter perturbation.With a useful lemma proved,the relation between global asymptotic stability and robust stability is established.Then with no strictly monotonic increasing quantity and boundedness required for the activation function,a suitable Lyapunov functional is framed properly to give a sufficient and robustly stable criterion in form of linear matrix inequality（LMI）for the equilibrium point of the concerned Cohen-Grossberg neural networks.Simulation result verify the effectiveness of the conclusion.