大连理工大学学报2017，Vol.57Issue(5)：537-544,8.DOI:10.7511/dllgxb201705015
带有常时滞循环耦合神经网络的全局指数稳定性
Global exponential stability of cycle associative neural network with constant delays
摘要
Abstract
The global exponential stability of cycle associative neural network with constant delays is discussed.During the discussion, by constructing homeomorphism mapping,it is demonstrated that there exists an equilibrium point which is unique for this system,then the global exponential stability of the unique equilibrium point is testified by constructing proper Lyapunov function.Similar to previous work about neural network stability,under the assumption that the activation function about neuron satisfies Lipschitz condition and the matrix constructed by correlation coefficient satisfies given condition,the dynamics of global exponential stability for n-layer neural network with constant delays are obtained.The results contain that when the passive rate of neuron is sufficiently large, the neural network is global exponential stable.关键词
指数稳定性/平衡点/神经网络/Lyapunov函数Key words
exponential stability/equilibrium point/neural network/Lyapunov function分类
数理科学引用本文复制引用
石仁祥..带有常时滞循环耦合神经网络的全局指数稳定性[J].大连理工大学学报,2017,57(5):537-544,8.基金项目
江苏省自然科学基金资助项目(BK20131285).Natural Science Foundation of Jiangsu (BK20131285). （BK20131285）
