Yang Zhanyu, Luo Biao, Liu Derong, Li Yueheng
The School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Neural Netw. 2017 Sep;93:143-151. doi: 10.1016/j.neunet.2017.05.003. Epub 2017 May 18.
In this paper, the synchronization of memristor-based neural networks with time-varying delays via pinning control is investigated. A novel pinning method is introduced to synchronize two memristor-based neural networks which denote drive system and response system, respectively. The dynamics are studied by theories of differential inclusions and nonsmooth analysis. In addition, some sufficient conditions are derived to guarantee asymptotic synchronization and exponential synchronization of memristor-based neural networks via the presented pinning control. Furthermore, some improvements about the proposed control method are also discussed in this paper. Finally, the effectiveness of the obtained results is demonstrated by numerical simulations.
本文研究了基于忆阻器的神经网络通过牵制控制实现具有时变延迟的同步问题。引入了一种新颖的牵制方法，以同步分别表示驱动系统和响应系统的两个基于忆阻器的神经网络。利用微分包含理论和非光滑分析对其动力学进行了研究。此外，通过所提出的牵制控制，推导了一些充分条件，以保证基于忆阻器的神经网络的渐近同步和指数同步。此外，本文还讨论了关于所提出控制方法的一些改进。最后，通过数值模拟验证了所得结果的有效性。
