具有时变时滞的离散时间随机神经网络的时滞相依稳定性分析

Delay-Dependent Stability Analysis for Discrete-Time Stochastic Neural Networks with Time-Varying Delays

研究了一类具有时变时滞的离散时间随机神经网络的稳定性问题.通过构造包含更多交叉项的新的Lya-punov泛函,将时滞区间等分为两个子区间,根据时滞函数所处不同的子区间更加细致地讨论了Lyapunov泛函中相应项导数的上界,利用不等式技巧,自由权矩阵和凸组合方法得到系统均方全局指数稳定新判据,结果具有更低的保守性,并举例说明了本文方法的有效性.

The problem of stability is studied for a class of discrete-time stochastic neural networks with time-varying delays in this paper.By constructing a new Lyapunov functional,which contains more information about the cross terms,dividing the time delay,s variation interval into two subintervals,the corresponding upper bound of the derivative of the Lyapunov functional is estimated delicately.By employing discrete Jensen inequality according to the time delay function in different subinterval,the free-weighting matrix and convex combination methods,a novel delay-dependent stability criterion for guarantying the global exponential stability in the mean square sense of the addressed neural networks is derived.The result of this paper is less conservative than some existing ones in the literature.A numerical examples are given to illustrate the effectiveness and the benefits of the proposed method in this paper.