具有加性时变延迟的复值神经网络的全局渐近稳定性
Global asymptotic stability of complex-valued neural networks with additive time-varying delays.
作者信息
Subramanian K, Muthukumar P
机构信息
Department of Mathematics, The Gandhigram Rural Institute - Deemed University, Gandhigram, Tamilnadu 624 302 India.
出版信息
Cogn Neurodyn. 2017 Jun;11(3):293-306. doi: 10.1007/s11571-017-9429-1. Epub 2017 Mar 18.
Abstract
In this paper, we extensively study the global asymptotic stability problem of complex-valued neural networks with leakage delay and additive time-varying delays. By constructing a suitable Lyapunov-Krasovskii functional and applying newly developed complex valued integral inequalities, sufficient conditions for the global asymptotic stability of proposed neural networks are established in the form of complex-valued linear matrix inequalities. This linear matrix inequalities are efficiently solved by using standard available numerical packages. Finally, three numerical examples are given to demonstrate the effectiveness of the theoretical results.
摘要
在本文中，我们广泛研究了具有泄漏延迟和加性时变延迟的复值神经网络的全局渐近稳定性问题。通过构造一个合适的Lyapunov-Krasovskii泛函并应用新发展的复值积分不等式，以复值线性矩阵不等式的形式建立了所提出神经网络全局渐近稳定性的充分条件。利用标准的可用数值软件包有效地求解了该线性矩阵不等式。最后，给出了三个数值例子以证明理论结果的有效性。
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