一类具有Leakage时滞的惯性Cohen-Grossberg神经网络的全局指数稳定性和Hopf分支  

Global exponential stability and Hopf bifurcation of inertial Cohen-Grossberg neural networks with time delays in leakage terms

作　　者：田晓红[1] 徐瑞[1] 王志丽[1] 

机构地区：[1]军械工程学院应用数学研究所,河北石家庄050003

出　　处：《高校应用数学学报（A辑）》2016年第4期428-440,共13页Applied Mathematics A Journal of Chinese Universities(Ser.A)

基　　金：国家自然科学基金(11371368;11071254;61305076);河北省自然科学基金(A2013506012;A2014506015)

摘　　要：研究一类具有Leakage时滞的惯性Cohen-Grossberg神经网络模型.通过构造适当的Lyapunov泛函得到了平衡点全局指数稳定的充分条件.通过分析特征方程,讨论了系统平衡点的局部稳定性,得出了系统Hopf分支存在的充分条件.最后对所得理论结果进行了数值模拟.In this paper, a class of inertial Cohen-Grossberg neural networks with time delays in leakage terms is investigated. By constructing the appropriate Lyapunov functional, su-cient condi-tions are obtained for the global exponential stability of the equilibrium. By analyzing the correspond-ing characteristic equation, the local stability of the equilibrium and the existence of Hopf bifurcation are established. Numerical simulations are carried out to illustrate the main results.

关 键 词：惯性Cohen-Grossberg神经网络模型 Leakage时滞 HOPF分支 全局指数稳定性 

分 类 号：O175.1[理学—数学]