Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays
Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays
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DOI：
10.1016/j.neunet.2013.10.001
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
S. Wen;Gang Bao;Z. Zeng;Yiran Chen;Tingwen Huang
中科院分区：
文献类型：
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作者：
S. Wen;Gang Bao;Z. Zeng;Yiran Chen;Tingwen Huang
基于模糊理论和李雅普诺夫方法，研究了一类基于忆阻器的变时滞递归神经网络的全局指数同步问题。首先，设计了一个基于忆阻器的递归神经网络。然后，考虑到忆阻器的状态依赖特性，一个新的模糊模型，采用并行分布补偿（PDC）提供了一种新的方法来分析复杂的记忆基于神经网络只有两个子系统。本文的结果与前人的结果进行了比较。结果表明，本文的结果改进和推广了以往文献中的结果。最后通过一个例子说明了所得结果的有效性.
This paper deals with the problem of global exponential synchronization of a class of memristor-based recurrent neural networks with time-varying delays based on the fuzzy theory and Lyapunov method. First, a memristor-based recurrent neural network is designed. Then, considering the state-dependent properties of the memristor, a new fuzzy model employing parallel distributed compensation (PDC) gives a new way to analyze the complicated memristor-based neural networks with only two subsystems. Comparisons between results in this paper and in the previous ones have been made. They show that the results in this paper improve and generalized the results derived in the previous literature. An example is also given to illustrate the effectiveness of the results.