用时滞分解方法研究时变时滞神经网络的全局渐近稳定性

Shenping Xiao, Lin-Xing Xu, Gang Chen, Lingshuang Kong
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摘要

本文研究了时滞相关神经网络的稳定性问题。引入了一种新的Lyapunov-Krasovskii泛函,从而得到了一些更优的时滞相关稳定性判据。此外,利用延迟分解技术,建立了一些新的绝对稳定条件,对已有的绝对稳定条件进行了改进。最后,通过数值算例验证了该方法的可行性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Globally asymptotic stability for neural networks with time-varying delay via delay-decomposition approach
In this article, the problem of the stability for delay-dependent neural networks is concerned. A new Lyapunov-Krasovskii functional is introduced, so as to obtain some more superior delay-dependent stability criterion. Moreover, by employing the delay decomposition technique, some novel absolute stability conditions are established, which refine and improve some existing ones. Finally, the feasibility and superiority of the proposed method is demonstrated by a numerical example.
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