1) SICNNs
分流抑制型细胞神经网络
1.
By means of suitable Lyapunov functionals,the exponential stability of periodic solutions for shunting inhibitory cellular neural networks(SICNNs)with delays and variable coefficients is studied.
利用适当的李亚普若夫泛函,研究了时滞分流抑制型细胞神经网络的周期解的指数稳定性。
2) shunting inhibitory cellular neural networks
分流抑制细胞神经网络
1.
By using the Banach fixed point theory and constructing the Lyapunov functional,the existence and global attractivity of almost periodic solutions for shunting inhibitory cellular neural networks with distributed delays are studied under more general conditions.
利用Banach不动点理论和Lyapunov函数方法,在较一般条件下研究了具有分布时滞的分流抑制细胞神经网络概周期解的存在性和全局吸引性,给出了新的判据,推广了已知文献的一些结果且易于在实际工程领域中验证。
2.
Some related results for the shunting inhibitory cellular neural networks are improved.
利用一些分析技巧,讨论了一类时滞动力系统解的指数收敛行为,证明了此类时滞动力系统的所有解都指数收敛到零点,改进了已有的关于分流抑制细胞神经网络的相关结论。
3) Shunting inhibitory cellular neural networks (SICNNs)
分路抑制神经网络(SICNNs)
4) cellular neural networks with delay(DCNN)
时滞型细胞神经网络(DCNN)
5) neutral-type cellular neural networks
中立型细胞神经网络
1.
Based on the Lyapunov-Krasovskii functional stability analysis and the linear matrix inequality(LMI) approach,the global exponential stability was discussed for the neutral-type cellular neural networks with discrete and distributed time-varying delays,a new sufficient condition was derived to assure the global exponential stability of the equilibrium.
利用Lyapunov-Krasovskii稳定性理论和线性矩阵不等式(LMI)处理方法,研究了一类具有混合时变时滞的中立型细胞神经网络的全局指数稳定性问题,获得了该模型平衡点全局指数稳定的一个新的充分条件。
补充资料：细胞的多样性神经胶质细胞
[图]
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条