Global exponential stability of discrete-time Cohen-Grossberg neural networks
Global exponential stability of discrete-time Cohen-Grossberg neural networks
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DOI：
10.1016/j.neucom.2004.08.004
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发表时间：
2005-03
期刊：
影响因子：
6
通讯作者：
Wenjun Xiong;Jinde Cao
中科院分区：
文献类型：
--
作者：
Wenjun Xiong;Jinde Cao
本文对连续时间Cohen-Grossberg神经网络（cgnn）的离散时间版本进行了阐述和研究。基于Lyapunov方法，得到了具有和不具有时滞的cgnn离散系统全局指数稳定性的几个充分条件。所得结果没有假设连接矩阵的对称性，激活函数的单调性和可微性。
Discrete-time versions of the continuous-time Cohen–Grossberg neural networks (CGNNs) are formulated and studied in this paper. Several sufficient conditions are obtained to ensure the global exponential stability of the discrete-time systems of CGNNs with and without delays based on Lyapunov methods. The obtained results have not assume the symmetry of the connection matrix, and monotonicity and the differentiability of the activation functions.