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Analysis of global O(t(-alpha)) stability and global asymptotical periodicity for a class of fractional-order complex-valued neural networks with time varying delays

机译:一类具有时变时滞的分数阶复值神经网络的全局O(t(-alpha))稳定性和全局渐近周期分析

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摘要

In this paper, the problem of the global O(t(-alpha)) stability and global asymptotic periodicity for a class of fractional-order complex-valued neural networks (FCVNNs) with time varying delays is investigated. By constructing suitable Lyapunov functionals and a Leibniz rule for fractional differentiation, some new sufficient conditions are established to ensure that the addressed FCVNNs are globally O(t(-alpha)) stable. Moreover, some sufficient conditions for the global asymptotic periodicity of the addressed FCVNNs with time varying delays are derived, showing that all solutions converge to the same periodic function. Finally, numerical examples are given to demonstrate the effectiveness and usefulness of our theoretical results. (C) 2016 Elsevier Ltd. All rights reserved.
机译:在本文中,研究了一类具有时变时滞的分数阶复值神经网络(FCVNN)的全局O(t(-α))稳定性和全局渐近周期的问题。通过构建适当的Lyapunov泛函和用于分数微分的Leibniz规则,建立了一些新的充分条件,以确保所寻址的FCVNN整体上是O(t(-α))稳定的。此外,推导了具有时变时延的寻址FCVNN的全局渐近周期性的一些充分条件,表明所有解都收敛到相同的周期性函数。最后,通过数值例子说明了我们理论结果的有效性和实用性。 (C)2016 Elsevier Ltd.保留所有权利。

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