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首页> 外文期刊>IEEE Transactions on Circuits and Systems. I, Regular Papers >New Sufficient Conditions for Global Robust Stability of Delayed Neural Networks
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New Sufficient Conditions for Global Robust Stability of Delayed Neural Networks

机译:时滞神经网络的全局鲁棒稳定性的新充分条件

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摘要

In this paper, we continue to explore application of nonsmooth analysis to the study of global asymptotic robust stability (GARS) of delayed neural networks. In combination with Lyapunov theory, our approach gives several new types of sufficient conditions ensuring GARS. A significant common aspect of our results is their low computational complexity. It is demonstrated that the reported results can be verified either by conducting spectral decompositions of symmetric matrices associated with the uncertainty sets of network parameters, or by solving a semidefinite programming problem. Nontrivial examples are constructed to compare with some closely related existing results
机译:在本文中,我们将继续探索非光滑分析在延迟神经网络的全局渐近鲁棒稳定性(GARS)研究中的应用。结合李雅普诺夫理论,我们的方法给出了确保GARS的几种新型充分条件。我们的结果的一个重要的共同点是计算复杂度低。结果表明,所报告的结果可以通过对与网络参数的不确定性集合相关的对称矩阵进行光谱分解或解决半定规划问题来验证。构建非平凡的示例以与一些紧密相关的现有结果进行比较

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