School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China.
School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China.
Neural Netw. 2018 Sep;105:1-13. doi: 10.1016/j.neunet.2018.04.008. Epub 2018 Apr 25.
This paper investigates the anti-synchronization of complex-valued memristor-based neural networks with time delays via designed external controllers. By constructing appropriate Lyapunov functions and using inequality technique, two different types of controllers are derived to guarantee the exponential anti-synchronization of complex-valued memristor-based delayed neural networks. Compared with existing relevant results, the proposed results of this paper are more general and less conservative. In addition, the presented theoretical results are easy to be checked with the parameters of systems themselves. Finally, an example with numerical simulations illustrates the effectiveness of the obtained results.
本文通过设计外部控制器研究了具有时滞的复值忆阻神经网络的反同步问题。通过构建合适的李雅普诺夫函数和使用不等式技术，导出了两种不同类型的控制器，以保证复值忆阻时滞神经网络的指数反同步。与现有的相关结果相比，本文提出的结果更具一般性，且更不保守。此外，所提出的理论结果易于用系统自身的参数进行检验。最后，通过数值模拟示例说明了所获得结果的有效性。
