Exponential stability of a class of complex-valued neural networks with time-varying delays
Exponential stability of a class of complex-valued neural networks with time-varying delays
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DOI：
10.1016/j.neucom.2015.02.024
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发表时间：
2015-09-21
期刊：
影响因子：
6
通讯作者：
Xie, Weichau
中科院分区：
文献类型：
--
作者：
Pan, Jie;Liu, Xinzhi;Xie, Weichau
研究了一类具有时变时滞的复值神经网络。利用复值神经网络的共轭系统和Brouwer不动点定理，得到了平衡点存在唯一的充分条件.利用时滞微分不等式，给出了复值神经网络平衡点全局指数稳定的判据。这些结果易于应用于复值神经网络的研究，无论其激活函数是否通过分离其真实的和虚部而显式表示。数值模拟的两个例子，突出所获得的结果的有效性。(C)2015 Elsevier B.V.版权所有。
This paper studies a class of complex-valued neural networks with time-varying delays. By using the conjugate system of the complex-valued neural networks and Brouwer's fixed point theorem, sufficient conditions to guarantee the existence and uniqueness of an equilibrium are obtained. Some criteria on globally exponential stability of the equilibrium of the complex-valued neural networks are also established by using a delay differential inequality. These results are easy to apply to the study of the complex-valued neural networks whether their activation functions are explicitly expressed by separating their real and imaginary parts or not. Two examples with numerical simulations are given to highlight the effectiveness of the obtained results. (C) 2015 Elsevier B.V. All rights reserved.