Li Xiaofan, Fang Jian-An, Li Huiyuan
School of Electrical Engineering, Yancheng Institute of Technology, Yancheng 224051, PR China; School of Information Science and Technology, Donghua University, Shanghai 201620, PR China.
School of Information Science and Technology, Donghua University, Shanghai 201620, PR China.
Neural Netw. 2017 Sep;93:165-175. doi: 10.1016/j.neunet.2017.05.008. Epub 2017 May 25.
This paper investigates master-slave exponential synchronization for a class of complex-valued memristor-based neural networks with time-varying delays via discontinuous impulsive control. Firstly, the master and slave complex-valued memristor-based neural networks with time-varying delays are translated to two real-valued memristor-based neural networks. Secondly, an impulsive control law is constructed and utilized to guarantee master-slave exponential synchronization of the neural networks. Thirdly, the master-slave synchronization problems are transformed into the stability problems of the master-slave error system. By employing linear matrix inequality (LMI) technique and constructing an appropriate Lyapunov-Krasovskii functional, some sufficient synchronization criteria are derived. Finally, a numerical simulation is provided to illustrate the effectiveness of the obtained theoretical results.
本文通过不连续脉冲控制研究了一类具有时变时滞的基于复值忆阻器的神经网络的主从指数同步问题。首先，将具有时变时滞的主从复值忆阻器神经网络转化为两个实值忆阻器神经网络。其次，构造并利用脉冲控制律来保证神经网络的主从指数同步。再次，将主从同步问题转化为主从误差系统的稳定性问题。通过运用线性矩阵不等式（LMI）技术并构造适当的Lyapunov-Krasovskii泛函，得到了一些充分的同步准则。最后，给出了数值仿真以说明所获理论结果的有效性。
