具脉冲马尔可夫跳中立型时滞神经网络事件触发状态估计

Event-Triggered State Estimation for Impulsive Markovian Jumping Neutral Type Delayed Neural Networks

研究了具脉冲和混合时滞的马尔可夫跳中立型神经网络的状态估计.首先,引入事件触发传输机制;其次,通过构造合适的Lyapunov-Krasovskii函数,利用詹森不等式、倒凸不等式、自由权矩阵和区间分割等技术,得到增广系统是指数稳定的充分条件;最后,通过数值算例验证了所提出结论的有效性.

State estimation of Markovian jumping neutral type neural network with impulsive and mixed delays is studied.Firstly,the event triggered transmission mechanism is introduced.Secondly,by constructing the appropriate Lyapunov-Krasovskii function,and by using the techniques of Jensen inequality,invert convex inequality,free weight matrix and interval partition,the sufficient conditions for the exponential stability of the augmented system are obtained.Finally,a numerical example shows the effectiveness of the conclusions.