随机变时滞神经网络的输入状态稳定性

摘要

给出了一种新的稳定性判据:基于线性矩阵不等式(LMI)的输入状态稳定性判据.基于模型变换,通过构造合适的Lyapunov函数,利用随机分析理论、伊藤(It?'s)公式和一些不等式方法,以线性矩阵不等式形式给出了随机变时滞神经网络的随机输入状态稳定的充分条件.然后通过数值算例说明所提出的方法有较小的保守性,表明所提出方法的有效性.%A new stability criterion based on the linear matrix inequality (LMI) input-to-state stability criterion is given. Based on model transformation, input-to-state stability of Stochastic neural networks with Time-Varying Delay is given a sufficient condition in form of linear matrix inequality by constructing appropriate Lyapunov-Krasovskii functional, stochastic analysis theory and It?'s formula and applying some inequality approach. Then, the proposed method is proven to be less conservative by illustrating with the numerical examples, which shows the effectiveness of the method.