基于忆阻器的 BAM 神经网络的时变离散时滞固定时间同步。
Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay.
机构信息
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
出版信息
Neural Netw. 2017 Dec;96:47-54. doi: 10.1016/j.neunet.2017.08.012. Epub 2017 Sep 11.
This paper is devoted to studying the fixed-time synchronization of memristor-based BAM neural networks (MBAMNNs) with discrete delay. Fixed-time synchronization means that synchronization can be achieved in a fixed time for any initial values of the considered systems. In the light of the double-layer structure of MBAMNNs, we design two similar feedback controllers. Based on Lyapunov stability theories, several criteria are established to guarantee that the drive and response MBAMNNs can realize synchronization in a fixed time. In particular, by changing the parameters of controllers, this fixed time can be adjusted to some desired value in advance, irrespective of the initial values of MBAMNNs. Numerical simulations are included to validate the derived results.
本文致力于研究具有离散时滞的忆阻双曲型细胞神经网络（MBAMNNs）的固定时间同步。固定时间同步意味着对于所考虑系统的任意初始值，都可以在固定时间内实现同步。根据 MBAMNNs 的双层结构，我们设计了两个类似的反馈控制器。基于 Lyapunov 稳定性理论，建立了几个准则来保证驱动和响应 MBAMNNs 可以在固定时间内实现同步。特别地，通过改变控制器的参数，可以预先将这个固定时间调整到某个期望的值，而与 MBAMNNs 的初始值无关。数值模拟验证了所得结果。
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