Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli
Global exponential stability of recurrent neural networks with time-varying delays in the presence of strong external stimuli
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DOI：
10.1016/j.neunet.2006.08.009
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发表时间：
2006-12
期刊：
影响因子：
--
通讯作者：
Z. Zeng;Jun Wang
中科院分区：
文献类型：
--
作者：
Z. Zeng;Jun Wang
本文给出了强外部激励下具有有界激活函数和有界时变时滞的递归神经网络的全局指数稳定性的新的理论结果。证明了当输入向量的绝对值超过某个标准时，Cohen-Grossberg神经网络是全局指数稳定的.作为特例，Hopfield神经网络和细胞神经网络进行了详细研究。此外，它表明，在这里的标准，如果部分满足，仍然可以使用与现有的稳定性条件相结合。仿真结果也讨论了两个说明性的例子。
This paper presents new theoretical results on the global exponential stability of recurrent neural networks with bounded activation functions and bounded time-varying delays in the presence of strong external stimuli. It is shown that the Cohen–Grossberg neural network is globally exponentially stable, if the absolute value of the input vector exceeds a criterion. As special cases, the Hopfield neural network and the cellular neural network are examined in detail. In addition, it is shown that criteria herein, if partially satisfied, can still be used in combination with existing stability conditions. Simulation results are also discussed in two illustrative examples.