Global Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
Global Stability of Complex-Valued Recurrent Neural Networks With Time-Delays
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DOI：
10.1109/tnnls.2012.2195028
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发表时间：
2012-06-01
影响因子：
10.4
通讯作者：
Wang, Jun
中科院分区：
文献类型：
--
作者：
Hu, Jin;Wang, Jun
在过去的十年中，一些复值神经网络已经被开发并应用于各个研究领域。作为实值递归神经网络的扩展，复值递归神经网络使用复值状态、连接权重或激活函数，这些函数具有比实值递归神经网络复杂得多的性质。本文给出了具有两类复值激活函数的时滞复值递归神经网络存在唯一平衡点、全局渐近稳定性和全局指数稳定性的几个充分条件。三个数值算例的仿真结果也证实了理论结果的有效性。
Since the last decade, several complex-valued neural networks have been developed and applied in various research areas. As an extension of real-valued recurrent neural networks, complex-valued recurrent neural networks use complex-valued states, connection weights, or activation functions with much more complicated properties than real-valued ones. This paper presents several sufficient conditions derived to ascertain the existence of unique equilibrium, global asymptotic stability, and global exponential stability of delayed complex-valued recurrent neural networks with two classes of complex-valued activation functions. Simulation results of three numerical examples are also delineated to substantiate the effectiveness of the theoretical results.