A unifying proof of global asymptotical stability of neural networks with delay

被引：8

作者：

Huang, YS ^{[1
]}

Wu, CW

机构：

[1] Pace Univ, Dept Math, Pleasantville, NY 10570 USA

[2] IBM Corp, TJ Watson Res Ctr, Yorktown Hts, NY 10598 USA

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2005年 / 52卷 / 04期

关键词：

asymptotical stability; delay equations; Lyapunov functional; neural networks;

D O I：

10.1109/TCSII.2004.842023

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We present some new global stability results of neural networks with delay and show that these results generalize recently published stability results. In particular, several different stability conditions in the literature which were proved using different Lyapunov functionals; are generalized and unified by proving them using the same Lyapunov functional. We also show that under certain conditions, reversing the directions of the coupling between neurons preserves the global asymptotical stability of the neural network.

引用

页码：181 / 184

页数：4

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