IEEE Trans Cybern. 2013 Dec;43(6):1796-806. doi: 10.1109/TSMCB.2012.2230441.
In this paper, the problem of sampled-data synchronization for Markovian jump neural networks with time-varying delay and variable samplings is considered. In the framework of the input delay approach and the linear matrix inequality technique, two delay-dependent criteria are derived to ensure the stochastic stability of the error systems, and thus, the master systems stochastically synchronize with the slave systems. The desired mode-independent controller is designed, which depends upon the maximum sampling interval. The effectiveness and potential of the obtained results is verified by two simulation examples.
本文研究了具有时变时滞和变采样的马尔可夫跳变神经网络的采样数据同步问题。在输入时滞方法和线性矩阵不等式技术的框架下，推导出了两个时滞相关的判据，以确保误差系统的随机稳定性，从而使主系统与从系统随机同步。设计了与期望模式无关的控制器，其取决于最大采样间隔。通过两个仿真示例验证了所获得结果的有效性和潜力。
