Global Asymptotic Stability for Delayed Neural Networks Using an Integral Inequality Based on Nonorthogonal Polynomials
Global Asymptotic Stability for Delayed Neural Networks Using an Integral Inequality Based on Nonorthogonal Polynomials
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DOI：
10.1109/tnnls.2017.2750708
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发表时间：
2018-09
影响因子：
10.4
通讯作者：
Xianming Zhang;Wen-Juan Lin-;Q. Han;Yong He;Min Wu
中科院分区：
文献类型：
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作者：
Xianming Zhang;Wen-Juan Lin-;Q. Han;Yong He;Min Wu
本文研究了一类具有时变时滞的神经网络的全局渐近稳定性。首先，通过引入一个含有非正交多项式的辅助向量，建立了一个基于松弛矩阵的积分不等式，并将已有的一个积分不等式作为其特例。其次，构造了一个新的Lyapunov-Krasovskii泛函，以适应所得到的积分不等式的应用。最后通过两个数值算例验证了该判据的有效性。
This brief is concerned with global asymptotic stability of a neural network with a time-varying delay. First, by introducing an auxiliary vector with some nonorthogonal polynomials, a slack-matrix-based integral inequality is established, which includes some existing one as its special case. Second, a novel Lyapunov–Krasovskii functional is constructed to suit for the use of the obtained integral inequality. As a result, a less conservative stability criterion is derived, whose effectiveness is finally demonstrated through two well-used numerical examples.