Output convergence analysis of continuous-time recurrent neural networks

被引：0

作者：

Liu, DR ^{[1
]}

Hu, SQ ^{[1
]}

机构：

[1] Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA

来源：

PROCEEDINGS OF THE 2003 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOL III: GENERAL & NONLINEAR CIRCUITS AND SYSTEMS | 2003年

关键词：

D O I：

暂无

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：

This paper discusses the global output convergence of a class of continuous-time recurrent neural networks with globally Lipschitz continuous and monotone nondecreasing activation functions and locally Lipschitz continuous time-varying thresholds. We establish several sufficient conditions to guarantee the global output convergence for this class of neural networks. The present results do not require symmetry in the connection weight matrix. These convergence results are useful in the design of the recurrent neural networks with time-varying thresholds.

引用

页码：466 / 469

页数：4

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