A New Criterion of Delay-Dependent Asymptotic Stability for Hopfield Neural Networks With Time Delay
A New Criterion of Delay-Dependent Asymptotic Stability for Hopfield Neural Networks With Time Delay
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DOI：
10.1109/tnn.2007.912593
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发表时间：
2008-03
影响因子：
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通讯作者：
Shaoshuai Mou;Huijun Gao;J. Lam;W. Qiang
中科院分区：
文献类型：
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作者：
Shaoshuai Mou;Huijun Gao;J. Lam;W. Qiang
本文研究时滞Hopfield神经网络(HNN)的全局渐近稳定性问题。通过引入一种新的Lyapunov-Krasovskii泛函，给出了一种新的渐近稳定性判据，并用线性矩阵不等式(LMI)表示，该判据易于用标准软件求解。这种基于延迟分步方法的新准则被证明是不那么保守的，并且通过细化延迟分步可以显着降低保守性。给出了一个算例，说明了该方法的有效性和优越性。
In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.