一类Markov跳变神经网络的时滞相关鲁棒稳定性

针对一类Markov跳变神经网络,研究了其在系统参数不确定情况下的全局鲁棒稳定性。利用Leibniz Newton公式对原系统进行等价变换,基于Lyapunov 稳定性理论,并结合Moon不等式得到了此类Markov跳变神经网络时滞相关均方鲁棒稳定性的判别条件。所得结果以线性矩阵不等式(linear matrix inequality, LMI)的形式给出,容易被Matlab中的LMI工具箱验证。最后,通过一个算例验证了所得结论的有效性。

Delay-dependent robust stability for a class of Markov jumping neural networks

The robust stability for a class of neural networks with Markov jumping parameters is investiga-ted. An equivalent transformation is made for the original system by the Leibniz-Newton formula. By applying Lyapunov stability theory and associating with Moon's inequality, the delay-dependent robust stability condition for the Markov jumping neural networks is established. The results are given in the form of linear matrix inequality (LMI) and can be solved readily by the LMI tool box of Matlab. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.