离散时间的混合时滞耦合神经网络的鲁棒指数同步

摘要

This paper addresses the analysis problem of synchronization for a class of discrete-time coupled neural networks with time-varying and distributed delays. The neural networks are subject to parameter uncertainty. Furthermore, the description of the activation functions is a more general sector nonlinear function than the recently commonly-used Lipschitz conditions, which are assumed to be neither differentiable nor strictly monotonic. By referring to Lyapunov functional method and Kronecker product technique, some sufficient conditions depending on delay are derived for robust exponential synchronization of such systems. Finally, a simulation example is presented to show the usefulness of the derived LMI-based synchronization scheme.%讨论了一类离散时间的时滞耦合神经网络的同步问题。在参数不确定的离散时间耦合神经网络中，考虑了变时滞和有限分布时滞。同时，细胞激活函数假设为较Lipschitz条件更为一般的扇形非线性函数，该函数可以既不可微又不严格单调。通过构造Lyapunov-Krasovskii泛函，运用线性矩阵不等式（LMI）技术，并结合Kronecker积来获得耦合神经网络鲁棒全局指数同步的充分性判据，并且所获得的判据依赖于时滞。最后，对一个实例进行仿真，说明结论的有效性。