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Global asymptotic stability of periodic solutions for delayed complex-valued Cohen-Grossberg neural networks by combining coincidence degree theory with LMI method

机译:符合度理论与LMI方法相结合的时滞复值Cohen-Grossberg神经网络周期解的全局渐近稳定性

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The paper is concerned with the existence and global asymptotic stability of periodic solutions for a class of delayed complex-valued Cohen-Grossberg neural networks. Without using the method of the a priori estimate of periodic solutions, by combining Mawhin's continuation theorem of coincidence degree theory with LMI method and using inequality techniques, a novel LMI-based sufficient condition on the existence of periodic solutions is established for the complex-valued Cohen-Grossberg neural networks. Then by using inequality techniques, a novel sufficient condition on the global asymptotic stability of periodic solutions for the above complex-valued neural networks is established. Our results and method are new and complementary to the existing papers on the study of periodic solutions of neural networks. (C) 2018 Elsevier B.V. All rights reserved.
机译:本文关注一类时滞复值Cohen-Grossberg神经网络周期解的存在性和全局渐近稳定性。在不使用周期解的先验估计的方法的情况下,通过将重合度理论的Mawhin连续定理与LMI方法结合并使用不等式技术,针对复值建立了基于LMI的新的关于周期解存在的充分条件Cohen-Grossberg神经网络。然后通过不等式技术,为上述复数值神经网络的周期解的全局渐近稳定性建立了一个新的充分条件。我们的结果和方法是新的,并且是对现有神经网络周期解研究论文的补充。 (C)2018 Elsevier B.V.保留所有权利。

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