Delay-independent exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms
Delay-independent exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms
复制标题
DOI：
10.1007/s11071-006-9164-3
复制
发表时间：
2007-01
影响因子：
5.6
通讯作者：
Xiaolin Li;Jinde Cao
中科院分区：
文献类型：
--
作者：
Xiaolin Li;Jinde Cao
与以前的方法不同，本文利用Halanay不等式技术，结合Lyapunov方法，建立了具有时变时滞和反应扩散项的随机Cohen-Grossberg神经网络指数稳定性的一个与时滞无关的充分条件。此外，对于具有或不具有反应扩散项的确定性时滞Cohen-Grossberg神经网络，也得到了其全局指数稳定性的充分判据。提出的结果改进和扩展了早期文献中的结果，并且更容易验证。最后通过一个算例说明了所得结果的正确性。
Different from the approaches used in the earlier papers, in this paper, the Halanay inequality technique, in combination with the Lyapunov method, is exploited to establish a delay-independent sufficient condition for the exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms. Moreover, for the deterministic delayed Cohen–Grossberg neural networks, with or without reaction–diffusion terms, sufficient criteria for their global exponential stability are also obtained. The proposed results improve and extend those in the earlier literature and are easier to verify. An example is also given to illustrate the correctness of our results.