含时滞的Hopfield网络模型的全局指数稳定性

Global exponential stability for delayed hopfield neural network models

本文研究了具有变时滞的细胞神经网络系统:xi(T)=-cixi(t)+n∑j+1aijfj(xj(t))+n∑ijbjfj(xj(t-τj))+Ii,1,2,…,n.在非线性神经激励函数是Lipschitz条件下,通过引入Lyapunov函数,用不等式等方法,得到了该系统的平衡点是全局指数稳定的条件.

In the paper, under the hypothesis that the nonlinear neural active fanctions are Lipschtz condition, the global exponential stability is stuied for the following class of delay Hopfield neural networks by a Lapunov functional and applying inequality： x^·i（t）=-cixi（t）＋∑^n k=1aijfj（xj（t））＋∑^n j=1bijfj（xj（t-τj））＋Ii,i=1,2,…,n