Global asymptotic stability analysis for neutral stochastic neural networks with time-varying delays
Global asymptotic stability analysis for neutral stochastic neural networks with time-varying delays
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DOI：
10.1016/j.cnsns.2008.04.001
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发表时间：
2009-04
影响因子：
3.9
通讯作者：
Weiwei Su;Yiming Chen
中科院分区：
文献类型：
--
作者：
Weiwei Su;Yiming Chen
研究了一类具有时变时滞和范数有界不确定性的中立型随机神经网络的全局渐近稳定性。基于Lyapunov稳定性理论和随机分析方法，导出了对于所有允许的参数不确定性，目标系统在均方处全局、鲁棒、渐近稳定的时滞相关判据。这些准则可以通过Matlab中的LMI控制工具箱轻松地进行检查。算例说明了所得结果的可行性和有效性。
In this paper, the global asymptotic stability is investigated for a class of neutral stochastic neural networks with time-varying delays and norm-bounded uncertainties. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criteria are derived to ensure the global, robust, asymptotic stability of the addressed system in the mean square for all admissible parameter uncertainties. The criteria can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the feasibility and effectiveness of the results.