含离散和分布时变时延神经网络系统的指数稳定性

针对一类含有离散和分布时延神经网络,在神经激活函数较弱的约束条件下,通过定义一个更具一般性的Lyapunov泛函,使用凸组合技术,得到了新的基于线性矩阵不等式表示的指数稳定性判据.与现有结果相比,这些判据具有较小的保守性.仿真算例表明,得到的结果是有效的且保守性小.

Exponential Stability for Neural Networks Systems with Discrete and Distributed Time-varying Delays

Under weaker assumptions on the activation functions,by defining a more general type of Lyapunov functionals and using the convex combination technique,the less conservative exponential stability criteria for a class of neural networks(NNs) with both discrete and distributed time-varying delays are established.The obtained conditions are dependent on both discrete and distributed delays,and are expressed in terms of linear matrix inequalities.Numerical examples show the effectiveness and less conservatism of the proposed conditions.