On exponential stability analysis for neural networks with time-varying delays and general activation functions
On exponential stability analysis for neural networks with time-varying delays and general activation functions
复制标题
DOI：
10.1016/j.cnsns.2011.08.016
复制
发表时间：
2012-03
影响因子：
3.9
通讯作者：
Yijing Wang;Cuili Yang;Z. Zuo
中科院分区：
文献类型：
--
作者：
Yijing Wang;Cuili Yang;Z. Zuo
研究一类具有区间时变时滞和一般激活函数的细胞神经网络的指数稳定性分析。激活函数的有界性假设不需要。放宽了时滞导数小于1的限制，时变时滞的下界不限制为零。建立了一个包含更多状态变量信息的Lyapunov-Krasovskii泛函，导出了一个新的指数稳定性判据。所得到的条件与已有的条件相比具有潜在的优势，因为在整个对Lyapunov泛函导数上界的估计过程中没有忽略任何有用的项。最后，给出了三个数值算例来说明所提出的设计方法和应用。
This paper is concerned with the exponential stability analysis for a class of cellular neural networks with both interval time-varying delays and general activation functions. The boundedness assumption of the activation function is not required. The limitation on the derivative of time delay being less than one is relaxed and the lower bound of time-varying delay is not restricted to be zero. A new Lyapunov–Krasovskii functional involving more information on the state variables is established to derive a novel exponential stability criterion. The obtained condition shows potential advantages over the existing ones since no useful item is ignored throughout the estimate of upper bound of the derivative of Lyapunov functional. Finally, three numerical examples are included to illustrate the proposed design procedures and applications.