LMI-based approach for delay-dependent exponential stability analysis of BAM neural networks
LMI-based approach for delay-dependent exponential stability analysis of BAM neural networks
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DOI：
10.1016/j.chaos.2004.09.037
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发表时间：
2005-05
影响因子：
7.8
通讯作者：
Xia Huang;Jinde Cao;De-shuang Huang
中科院分区：
文献类型：
--
作者：
Xia Huang;Jinde Cao;De-shuang Huang
基于Lyapunov-Krasovskii泛函和线性矩阵不等式(LMI)方法，给出了一组判定BAM神经网络指数稳定性的判据。这些判据清楚地表明了时滞对指数收敛速度的影响，并说明了兴奋效应和抑制效应之间的差异。此外，所得结果对于确定时滞BAM网络的指数稳定性很容易得到验证，并且比前人的结果具有更少的保守性和更少的限制。
Based on the Lyapunov–Krasovskii functionals in combination with linear matrix inequality (LMI) approach, a set of criteria are proposed for the exponential stability of BAM neural networks with constant or time-varying delays. These criteria manifest explicitly the influence of time delay on exponential convergence rate and show the differences between the excitatory and inhibitory effect. In addition, the obtained results are easily verified for determining the exponential stability of delayed BAM networks and impose less conservative and less restrictive than the ones in previous papers.