Exponential stability of recurrent neural networks with both time-varying delays and general activation functions via LMI approach
Exponential stability of recurrent neural networks with both time-varying delays and general activation functions via LMI approach
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DOI：
10.1016/j.neucom.2007.08.024
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发表时间：
2008-08
期刊：
影响因子：
6
通讯作者：
Q. Song
中科院分区：
文献类型：
--
作者：
Q. Song
本文研究了具有一般激活函数的变时滞递归神经网络的指数稳定性分析问题。既不假设这些激活函数的有界性和单调性，也不假设时变时滞的可微性。利用李雅普诺夫泛函和自由权矩阵方法，得到了线性矩阵不等式形式的神经网络平衡点存在唯一性和全局指数稳定性的充分条件.此外，指数收敛速度指标的估计，这取决于系统参数。所提出的稳定性结果比文献中最近已知的一些结果保守性更小，这是通过一个例子与仿真证明。
In this paper, the problem on exponential stability analysis of recurrent neural networks with both time-varying delays and general activation functions is considered. Neither the boundedness and the monotony on these activation functions nor the differentiability on the time-varying delays are assumed. By employing Lyapunov functional and the free-weighting matrix method, several sufficient conditions in linear matrix inequality form are obtained to ensure the existence, uniqueness and global exponential stability of equilibrium point for the neural networks. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The proposed stability results are less conservative than some recently known ones in the literature, which is demonstrated via an example with simulation.