Further Investigate the Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
Further Investigate the Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
复制标题
进一步研究时滞复值循环神经网络的稳定性
DOI：
10.1109/tnnls.2013.2294638
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发表时间：
2014-09-01
影响因子：
10.4
通讯作者：
Sun, Jitao
中科院分区：
文献类型：
--
作者：
Fang, Tao;Sun, Jitao
本文指出了最近发表的一篇关于复值递归神经网络（RNNs）的论文中的两个错误。此外，提出了一个新的复值激活函数条件，该条件比文献中广泛假设的Lipschitz条件保守性更小。基于新的条件和线性矩阵不等式，建立了具有时滞的复值rnn平衡点的存在唯一性和全局渐近稳定性的新准则。算例说明了理论结果的有效性。
This brief points out two mistakes in a recently published paper on complex-valued recurrent neural networks (RNNs). Moreover, a new condition for the complex-valued activation function is presented, which is less conservative than the Lipschitz condition that is widely assumed in the literature. Based on the new condition and linear matrix inequality, some new criteria to ensure the existence, uniqueness, and globally asymptotical stability ofthe equilibrium point of complex-valued RNNs with time delays are established. A numerical example is given to illustrate the effectiveness of the theoretical results.