New delay dependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays
New delay dependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays
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DOI：
10.1016/j.jfranklin.2012.03.007
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发表时间：
2012-08
期刊：
影响因子：
--
通讯作者：
R. Raja;R. Samidurai
中科院分区：
文献类型：
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作者：
R. Raja;R. Samidurai
研究了一类同时具有离散和分布时变时滞的时滞随机递归神经网络的稳定性分析问题。通过构造一个合适的Lyapunov-Krasovskii泛函，利用线性矩阵不等式（LMI）方法，给出了保证系统在均方意义下全局鲁棒渐近稳定的充分条件.这里得到的条件表示为线性矩阵不等式，其可行性可以很容易地检查由MATLAB LMI控制工具箱。此外，给出了两个数值例子与比较结果证明所得到的稳定性结果。
This paper is concerned with the stability analysis problem for a class of delayed stochastic recurrent neural networks with both discrete and distributed time-varying delays. By constructing a suitable Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to ensure the global, robust asymptotic stability for the addressed system in the mean square. The conditions obtained here are expressed in terms of LMIs whose feasibility can be checked easily by MATLAB LMI Control toolbox. In addition, two numerical examples with comparative results are given to justify the obtained stability results.