Stability Analysis of Markovian Jumping Stochastic Cohen–Grossberg Neural Networks With Mixed Time Delays
Stability Analysis of Markovian Jumping Stochastic Cohen–Grossberg Neural Networks With Mixed Time Delays
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DOI：
10.1109/tnn.2007.910738
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发表时间：
2008-02
影响因子：
--
通讯作者：
Huaguang Zhang;Yingchun Wang
中科院分区：
文献类型：
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作者：
Huaguang Zhang;Yingchun Wang
在这封信中，对于一类马尔可夫的随机Cohen-Grossberg神经网络（CGNN），考虑了全球渐近稳定性分析问题，其中包括离散延迟和分布式延迟，包括分散延迟和分布式延迟。基于线性矩阵不等式（LMI）技术，建立了替代延迟依赖性稳定性分析结果，可以通过使用数值有效的MATLAB LMI工具箱来轻松地检查其。不需要通过Newton-Leibniz公式进行系统转换和自由重量矩阵。包括两个数值示例以显示结果的有效性。
In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.