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具有各种激活函数和混合时变延迟的神经网络的指数镇定

Exponential stabilization of neural networks with various activation functions and mixed time-varying delays.

作者信息

Phat V N, Trinh H

机构信息

Institute of Mathematics, Vietnam Academy of Science and Technology, Hanoi 10307, Vietnam.

出版信息

IEEE Trans Neural Netw. 2010 Jul;21(7):1180-4. doi: 10.1109/TNN.2010.2049118. Epub 2010 Jun 14.

DOI:10.1109/TNN.2010.2049118
PMID:20550986
Abstract

This paper presents some results on the global exponential stabilization for neural networks with various activation functions and time-varying continuously distributed delays. Based on augmented time-varying Lyapunov-Krasovskii functionals, new delay-dependent conditions for the global exponential stabilization are obtained in terms of linear matrix inequalities. A numerical example is given to illustrate the feasibility of our results.

摘要

本文给出了具有各种激活函数和时变连续分布延迟的神经网络全局指数稳定化的一些结果。基于增广的时变Lyapunov-Krasovskii泛函，通过线性矩阵不等式得到了全局指数稳定化的新的时滞依赖条件。给出了一个数值例子来说明我们结果的可行性。

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