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Robust stability of discrete-time stochastic BAM neural networks with Markovian jumping parameters and time-varying delays

机译:基本跳跃参数的离散时间随机BAM神经网络的鲁棒稳定性和时变延迟

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This paper investigates the problem of robust stability for a class of uncertain discrete-time stochastic bidirectional associative memory(BAM) neural networks with Markovian jumping parameters and time-varying delays. By employing the Lyapunov functional we can get novel robust stability conditions in terms of linear matrix inequality (LMI), which can be easily solved by MATLAB LMI toolbox. Furthermore, we will introduce into some free weighting matrices in order to lead to much less conservative results. At last, one numerical example is given to illustrate the effectiveness of the proposed results.
机译:本文调查了一类不确定的离散时间随机双向双向联想内存(BAM)神经网络的鲁棒稳定性问题,与马尔可夫跳跃参数和时变延迟。通过使用Lyapunov功能,我们可以在线性矩阵不等式(LMI)中获得新的稳定稳定条件,可以通过Matlab LMI工具箱轻松解决。此外,我们将引入一些自由加权矩阵,以导致更少的保守结果。最后,给出了一个数值例子来说明所提出的结果的有效性。

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