基于时变时滞和脉冲的惯性忆阻器神经网络的全局指数稳定性。
Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses.
机构信息
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Department of Electronics and Information Engineering, Southwest University, Chongqing 400715, China; Key Laboratory of Machine Perception and Children's Intelligence Development, Chongqing University of Education, Chongqing, 400067, PR China.
Department of Mathematics, Texas A&M University at Qatar, Doha, P.O. Box 23874, Qatar.
出版信息
Neural Netw. 2017 Nov;95:102-109. doi: 10.1016/j.neunet.2017.03.012. Epub 2017 Apr 13.
In this study, we investigate the global exponential stability of inertial memristor-based neural networks with impulses and time-varying delays. We construct inertial memristor-based neural networks based on the characteristics of the inertial neural networks and memristor. Impulses with and without delays are considered when modeling the inertial neural networks simultaneously, which are of great practical significance in the current study. Some sufficient conditions are derived under the framework of the Lyapunov stability method, as well as an extended Halanay differential inequality and a new delay impulsive differential inequality, which depend on impulses with and without delays, in order to guarantee the global exponential stability of the inertial memristor-based neural networks. Finally, two numerical examples are provided to illustrate the efficiency of the proposed methods.
在本研究中，我们研究了具有脉冲和时变时滞的基于惯性忆阻器的神经网络的全局指数稳定性。我们基于惯性神经网络和忆阻器的特性构建了基于惯性忆阻器的神经网络。在同时对惯性神经网络进行建模时，考虑了有和无时延的脉冲，这在当前的研究中具有重要的实际意义。在 Lyapunov 稳定性方法的框架下，我们推导出了一些充分条件，以及一个扩展的 Halanay 微分不等式和一个新的时滞脉冲微分不等式，它们取决于有和无时延的脉冲，以保证基于惯性忆阻器的神经网络的全局指数稳定性。最后，我们提供了两个数值示例来说明所提出方法的有效性。
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