Exponential synchronization of delayed Markovian jump complex networks with generally uncertain transition rates
Exponential synchronization of delayed Markovian jump complex networks with generally uncertain transition rates
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DOI：
10.1016/j.amc.2015.09.032
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
R. Xu;Y. Kao;Cunchen Gao
中科院分区：
文献类型：
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作者：
R. Xu;Y. Kao;Cunchen Gao
研究了一类具有一般不确定转移速率的马尔可夫跳跃复杂网络(MJCN)的指数同步问题。在这种GUTR神经网络模型中，每个转换率可以是完全未知的，也可以只知道它的估计值。这种新的不确定性模型可以应用于许多实际案例。基于Lyapunov泛函方法和Kronecker乘积技术，以线性矩阵不等式(LMI)的形式给出了一个均方指数同步的充分条件，该条件可以用MatLab的LMI工具箱方便地求解。最后，通过一个数值算例验证了所提方法的有效性。
This paper investigates the exponential synchronization problem for a class of Markovian jump complex networks(MJCNs) with generally uncertain transition rates(GUTRs). In this GUTR neural network model, each transition rate can be completely unknown or only its estimate value is known. This new uncertain model could be applied to many practical cases. Based on the Lyapunov functional method and Kronecker product technique, a sufficient condition on the exponentially synchronization in mean square is derived in terms of linear matrix inequalities (LMIs)-which can be easily solved by using the Matlab LMI toolbox. Finally, one numerical example is well-studied to illustrate the effectiveness of the developed method.