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Stability conditions for uncertain BAM neural networks of neutral-type with time-varying delays

机译:时变时滞中立型不确定BAM神经网络的稳定性条件

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This paper deals with the robust stability for uncertain bidirectional associative memory (BAM) neural networks of neutral-type with time-varying delays. The parameter uncertainties are assumed to be norm bounded, the discrete delays and neutral delays are time-varying delays. The combined method, which is based on the Lyapunov-Krasovskii functional (LKF) combined with some inequality techniques, is used to investigate this problem. Then, by constructing a new LKF, using the Newton-Leibniz formula and introducing some free weighting matrices, some sufficient conditions are proposed to guarantee global asymptotic robust stability for the considered systems. It is also shown that the obtained results can be established in terms of linear matrix inequality (LMI).
机译:本文研究具有时变时滞的中立型不确定双向联想记忆(BAM)神经网络的鲁棒稳定性。假定参数不确定性是范数有界的,离散延迟和中性延迟是时变延迟。基于Lyapunov-Krasovskii泛函(LKF)和一些不等式技术的组合方法用于研究此问题。然后,通过使用牛顿-莱布尼兹(Newton-Leibniz)公式构造一个新的LKF并引入一些自由加权矩阵,提出了一些足以保证所考虑系统的全局渐近鲁棒稳定性的条件。还表明,可以根据线性矩阵不等式(LMI)建立获得的结果。

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