Cohen-Grossberg型BAM神经网络指数稳定性

Exponential stability for Cohen-Grossberg-type BAM neural networks

为研究具有分布时滞的并带有脉冲的Cohen-Grossberg型BAM神经网络的全局指数稳定性问题,主要方法是构造一个恰当的Lyapunov泛函以及通过利用一些函数分析的技巧,得到了所研究系统平衡点的全局指数稳定的充分条件的判据.证明过程中将激活函数的单调性和连续可微性的假设削弱为只需要满足Lipschitz条件,推广了已有文献的一些结果,使模型的应用更具有一般性.实例分析表明了所得结论的有效性.

In this paper,the global exponential stability of Cohen-Grossberg-type BAM neural networks with distributed delays and impulse is investigated.By using Lyapunov function and some analytical techniques,some sufficient conditions for the global exponential stability of equilibrium are obtained.In the proof process,the monotonicity and continuous differentiability of activation functions are reduced to meet the Lipschitz condition.The case study demonstrates the effectiveness of the criteria presented.