摘要
通过引入更加广泛的一类Lyapunov-Krasovskii泛函,并利用线性矩阵不等式分析技巧,得到了关于可变时滞广义神经网络模型平衡位置全局指数稳定的最新判据,并且判别条件中不限制连接权矩阵符号.该结果精炼和推广了一些已有的结果,并且更少保守,在实践上易于程序实现,方便使用.
Global exponential stability of recurrent neural networks with time-varying delays are studied. By using Lyapunov-Krasovskii functional and linear matrix inequality technique, we obtain a new sufficient conditions which does not restrict the sign of the connection weight matrix, for the networks to converge exponentially toward the equilibria. Compare with the method of Lyapunov functionals as in most previous studies, our method is simple in programming and less conservative than the ones reported so far in the literature.
出处
《河北大学学报(自然科学版)》
CAS
北大核心
2006年第2期123-126,138,共5页
Journal of Hebei University(Natural Science Edition)
基金
河北省自然科学基金资助项目(A200400089)