Global asymptotic and robust stability of recurrent neural networks with time delays
Global asymptotic and robust stability of recurrent neural networks with time delays
复制标题
DOI：
10.1109/tcsi.2004.841574
复制
发表时间：
2005-02-01
影响因子：
5.1
通讯作者：
Wang, J
中科院分区：
文献类型：
--
作者：
Cao, JD;Wang, J
本文研究了时滞神经网络的全局渐近稳定性(GAS)和全局鲁棒稳定性(GRS)这两个相关问题。首先，基于Lyapunov方法和线性矩阵不等式讨论了时滞神经网络的遗传算法。给出了确定时滞神经网络气体的新判据。在神经网络的设计和应用中，需要考虑网络参数有界摄动的偏差效应。在这种情况下，延迟神经网络必须被表示为区间神经网络模型。利用一个新的Lyapunov函数和矩阵不等式，得到了时滞区间神经网络平衡点的存在唯一性和GRS的几个充分条件。这些结果比以前的参考文献中给出的限制要少一些。
In this paper, two related problems, global asyrmptotic stability (GAS) and global robust stability (GRS) of neural networks with time delays, are studied. First, GAS of delayed neural networks is discussed based on Lyapunov method and linear matrix inequality. New criteria are given to ascertain the GAS of delayed neural networks. In the designs and applications of neural networks, it is necessary to consider the deviation effects of bounded perturbations of network parameters. In this case, a delayed neural network must be formulated as a interval neural network model. Several sufficient conditions are derived for the existence, uniqueness, and GRS of equilibria for interval neural networks with time delays by use of a new Lyapunov function and matrix inequality. These results are less restrictive than those given in the earlier references.