Cohen-Grossberg神经网络基于离散时间状态观测的几乎必然指数稳定

Almost sure exponential stability of Cohen-Grossberg neural networks based on discrete-time state observations

基于离散时间状态观测,研究带Markov切换的随机Cohen-Grossberg神经网络稳定的问题.通过构造Lyapunov函数,利用Ito微分公式、Borel-Cantelli’s引理及稳定性分析理论,得到非线性和线性系统几乎必然指数稳定的充分条件.最后,通过一个例子验证所得结果的可行性.

The paper investigates the stability problem of stochastic Cohen-Grossberg neural networks with Markov switching based on the observations of discrete-time state.We get a set of sufficient conditions of the almost sure exponential stability of nonlinear and linear system by constructing Lyapunov function,using Itô differential formula,Borel-Cantelli’s lemma and methods of stability analysis.Finally,an example is provided to illustrate the feasibility of obtained results.