Dynamic behaviors of memristor-based delayed recurrent networks
Dynamic behaviors of memristor-based delayed recurrent networks
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DOI：
10.1007/s00521-012-0998-y
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发表时间：
2013-09
影响因子：
6
通讯作者：
S. Wen;Z. Zeng;Tingwen Huang
中科院分区：
文献类型：
--
作者：
S. Wen;Z. Zeng;Tingwen Huang
研究了忆阻时滞网络周期解的存在性和全局指数稳定性问题。基于忆阻器和递归神经网络的相关知识，建立了忆阻器递归神经网络模型。得到了基于忆阻器的时滞递归网络周期解存在和全局指数稳定的充分条件。这些结果确保了基于忆阻器的网络在Filippov解意义下的全局指数稳定性。并且，利用所得结果可以方便地估计该网络的指数收敛速度。最后给出了一个算例，说明了理论结果的有效性.
This paper investigates the problem of the existence and global exponential stability of the periodic solution of memristor-based delayed network. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent networks is established. Several sufficient conditions are obtained, which ensure the existence of periodic solutions and global exponential stability of the memristor-based delayed recurrent networks. These results ensure global exponential stability of memristor-based network in the sense of Filippov solutions. And, it is convenient to estimate the exponential convergence rates of this network by the results. An illustrative example is given to show the effectiveness of the theoretical results.