基于忆阻器的递归神经网络的同步控制与扰动。
Synchronization control of memristor-based recurrent neural networks with perturbations.
机构信息
School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
出版信息
Neural Netw. 2014 May;53:8-14. doi: 10.1016/j.neunet.2014.01.010. Epub 2014 Jan 28.
In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results.
本文研究了具有脉冲扰动或边界扰动的忆阻递归神经网络的同步控制。我们发现，忆阻连接权重与系统的稳定性有一定的关系。得到了一些准则来保证忆阻神经网络具有较强的抗噪声能力。设计了两种控制器，使受扰动的忆阻神经网络能够收敛到平衡点，从而引发人的记忆模式。本文的分析采用了微分包含理论和李雅普诺夫函数方法。数值例子表明了我们结果的有效性。
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