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Robust stability of uncertain stochastic complex-valued neural networks with additive time-varying delays

机译:具附加时变时滞的不确定随机复数值神经网络的鲁棒稳定性

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In this paper, the robust stability problem for a class of uncertain stochastic complex-valued neural networks (USCVNNs) with additive time-varying delays (ATDs) is discussed. By constructing a suitable Lyapunov-krasovskii functional (LKF), more time delay information is considered. By employing integral inequalities, some delay-dependent stability criteria are derived by converting USCVNNs into an equivalent real-valued uncertain stochastic neural networks. The obtained stability criterion is presented in the form of linear matrix inequalities (LMIs), which can be calculated through MATLAB LMI toolbox. Finally, the validity and feasibility of the proposed method are demonstrated by two numerical examples.
机译:本文讨论了一类带有加性时变时延(ATD)的不确定随机复值神经网络(USCVNN)的鲁棒稳定性问题。通过构建合适的Lyapunov-krasovskii功能(LKF),可以考虑更多的时延信息。通过使用积分不等式,通过将USCVNN转换为等效的实值不确定随机神经网络,得出了一些依赖于延迟的稳定性准则。所获得的稳定性标准以线性矩阵不等式(LMI)的形式表示,可以通过MATLAB LMI工具箱进行计算。最后,通过两个数值例子验证了该方法的有效性和可行性。

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